text:
|Publication number
|Publication type
|Application number
|Publication date
|Mar 9, 2004
|Filing date
|Apr 2, 1999
|Priority date
|Apr 2, 1999
|Fee status
|Publication number
|Joseph L. Hellerstein
|Original Assignee
|International Business Machines Corporation
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (21), Non-Patent Citations (6), Referenced by (19), Classifications (11), Legal Events (7)
|External Links: USPTO, USPTO Assignment, Espacenet
The present invention relates generally to automating data analysis tasks and, more particularly, to analysis tasks that require navigation between dynamic data that has dissimilar structures.
The present invention provides for systems and methods for automatically navigating between dynamic data that has dissimilar structures. The term "dynamic" as used herein refers to frequent change with respect to data, a characteristic that affects the efficiency of navigation techniques. The term "dissimilar structure" as used herein refers to a data structure containing information that is not present in another data structure. Thus, it is said that the first data structure is dissimilar with respect to the second data structure. A problem in the management of distributed systems is described below to illustrate the prior art background. However, it is to be appreciated that the invention has broader applications.
Rapid improvements in both hardware and software have dramatically changed the cost structure of information systems. Today, hardware and software account for a small fraction of these costs, typically less than 20 percent (and declining). The remaining costs relate to the management of information systems, such as software distribution, providing help desk support, and managing quality of service (QoS).
Decision support is critical to the management of information systems. For example, in software distribution, we need to know: (i) which machines require software upgrades; (ii) what are the constraints on scheduling upgrades; and (iii) the progress of upgrades once installation has begun. In QoS management, decision support detects QoS degradations, identifies resource bottlenecks, and plans hardware and software acquisitions to meet future QoS requirements.
Accomplishing these tasks requires a variety of information, such as, for example, QoS measurements, resource measurements (e.g., network utilizations), inventory information, and topology specifications. Collectively, we refer to these information sources as data. Much of this data is dynamic. Indeed, measurement data changes with each collection interval. Further, in large networks, topology and inventory information change frequently due to device failures and changes made by network administrators.
We use the term "dataset" to describe a collection of data within the same structure. For example, a dataset might be organized as a relational table that is structured so that each row has the same columns. Here the data is structured into rows such that each row has a value for every column. A dataset contains multiple "data elements" (hereinafter, just elements), which are instances of data structured in the manner prescribed by the dataset (e.g., a row in a relational table). A group of elements within the dataset is called an "element collection." An element collection is specified by a "collection descriptor" (e.g., SQL where-clause for a relational table or line numbers for a sequential file). A collection descriptor consists of zero or more "constraints" that describe an element collection. A constraint consists of an "attribute" (e.g., a column name in a relational table or a field in a variable-length record), a relational operator (e.g., =, <, >), and a value.
Due to the diversity of software tools, administrative requirements, and other factors, data is typically grouped into multiple datasets. Thus, decision support often requires navigating from an element collection in one dataset to one or more element collections in other datasets. We refer to these as the "source element collection," "source dataset," "target element collections" and "target datasets," respectively.
With this background, we state one of the problems addressed by the present invention. We are given a source element collection and multiple target datasets. The objective is to find the target element collection that "best matches" the source element collection. By best matches, it is meant that the structure and content of the source element collection is the most similar to that of the target element collection.
To illustrate the problem addressed, we describe a scenario in QoS management. Considered is a situation in which end-users experience poor quality of service as quantified by long response times. The objective is to characterize the cause of long response times by: (i) when they occur; (ii) who is affected; (iii) which configuration elements are involved; and (iv) what components of the configuration element account for most of the delays.
The analyst starts with a dataset containing end-to-end response times. The dataset is structured into the following columns: shift, hour, subnet, host, user's division, user's department, user name, transaction issued, and response time. The analyst proceeds as follows:
Step 1. The analyst isolates the performance problem. This may be done in any conventional manner, such as, for example, is described in R. F. Berry and J. L. Hellerstein, "A Flexible and Scalable Approach to Navigating Measurement Data in Performance Management Applications," Second IEEE Conference on Systems Management, Toronto, Canada, June, 1996. In the example, isolation determines that poor response times are localized to the element collection described by the constraints: shift=1, hour=8, subnet=9.2.15, division=25, department=MVXD, user=ABC, and transaction=_XX. At this point, the analyst has characterized when the problem occurs, who is affected, and which configuration elements are involved.
Step 2. To determine what components of the configuration element account for most of the delays, the analyst must examine one or more other datasets. After some deliberation and investigation by the analyst, the analyst selects a dataset of operating system (OS) measurements that are structured as follows: hour, minute, shift, subnet, division, department, efficiency, waiting time, CPU waits, I/O waits, page waits, and CPU execution times.
Step 3. The analyst selects the subset of the OS data that best corresponds to the response time data. Doing so requires dealing with two issues: (i) the source and target datasets are structured somewhat differently in that the first has transaction information (which the second does not), and the second reports time in minutes (which the first does not); and (ii) the second dataset does not have records for user ABC, the user for which a problem was isolated. To resolve the first problem, the analyst decides to use only the information common to both datasets. So, transaction information and minutes are ignored when navigating from the response time data to the OS data. The second problem is resolved by assuming that users within the same department are doing similar kinds of work. Thus, the target element collection is described by the constraints: shift=1, hour=8, subnet=9.2.15, department=MVXD, and user=ABC.
Step 4. The analyst uses the OS data to characterize the host component that contributes the most to response time problems. This characterization reveals that paging delays account for a large fraction of end-to-end response times.
Steps 1 and 4 employ similar problem isolation logic. Indeed, automation exists for these steps. Unfortunately, in the prior art, steps 2 and 3 are performed manually. As such, these steps impede the automation, accuracy and comprehensiveness of problem isolation. This, in turn, significantly increases management costs. The challenges raised by steps 2 and 3 above are modest if there are a small number of measurement sources. Unfortunately, the number of measurement sources is large and growing.
Disimilarities in the structure of datasets typically arise because measurements are specific to the measured entity. Hence, heterogeneous equipment means heterogeneous measurement sources. Heterogeneity includes the different types of devices (e.g., routers versus file servers), different vendors, and different versions of the same product from the same vendor. With rapid changes in information technology, acquisition cycles are now much longer than technology cycles. For example, depreciation times for personal computers are typically 3 to 5 years, but the technology changes every 9 to 12 months. Further, customers typically upgrade hardware and software in an incremental fashion. The combination of these factors means that customers have an increasingly diverse collection of hardware and software. As such, the diversity of measurement sources is rapidly increasing.
One proposed way to attempt to avoid the problem of heterogeneous measurement sources is to build a data warehouse that integrates data with dissimilar structure, as described in R. Kimbell, "The Data Warehouse Toolkit," John Wiley and Sons, 1996. This is accomplished by employing tools that translate data formats and semantics into a common structure. Such an approach works well for fairly static data that is analyzed frequently in that the cost of building and maintaining the data warehouse is amortized over a long time window and a large number of data accesses. However, in systems management applications, the data is dynamic, such as QoS measurements that change every minute. Also, the detailed data used for solving QoS problems is only needed when a problem arises. Thus, the cost of building and maintaining the data warehouse far outweighs the benefits provided.
Existing art for navigating between datasets is specific to the manner in which the data is organized, that is, the conceptual model employed. We consider three such conceptual models: relational data (with variations), multidimensional databases (MDDB), and text documents. While other organizations may exist (e.g., graphical structures, such as hyper linked documents), similar issues arise in these organizations as well.
Considered first is data organized as relational tables. Here, a dataset is a table, an element is a row in a table, and a collection descriptor is an SQL where-clause. Navigation between datasets is accomplished through SQL queries that use the join operation. A join requires specifying the table to navigate to (e.g., in the from clause of SQL queries) and the join attributes used in the where clause. However, often times situations exist in which neither the table to navigate to nor the choice of join attributes are known. Thus, while it may be useful to employ join operations in an attempt to achieve automated navigation, the join operation itself does not solve the existing problems.
Considered next is data organized as MDDB, as described in R. F. Berry and J. L. Hellerstein, "A Flexible and Scalable Approach to Navigating Measurement Data in Performance Management Applications," Second IEEE Conference on Systems Management, Toronto, Canada, June, 1996. Conceptually, such an organization can be viewed as a layer on top of the relational model. The MDDB structures attributes into dimensions. Within a dimension, attributes may be further structured into a directed acyclic graph (DAG). Here, a dataset is a cube (a MDDB schema along with its base data), an element is a cell within a cube, and a collection descriptor is a where clause that abides by the hierarchical structure imposed by the MDDB. In the example above, there might be dimensions for Time, Configuration Element, Workload and Metric. In the source dataset, the Workload dimension may contain the attributes division, department, user, and transaction, ordered in this manner. Thus, the coordinate for this dimension would be division=25, department=MVXD, user=ABC, and transaction=_XX.
Navigation within a multidimensional database is accomplished by drill-down and drill-up operations. Drill-down adds constraints in one or more dimensions such that the attribute of the constraint is at the next lower level in the dimension's DAG. Drill-up is the inverse operation. It removes constraints in one or more dimensions. The attributes of the constraints removed are at the next higher level in the dimension hierarchies. For example, consider the constraints for the Workload dimension: division=25, department=MVXD, user=ABC, and transaction=_XX. Drill-up yields the constraints division=25, department=MVXD, and user=ABC.
Navigation between cubes can be accomplished in many ways. One approach is to use SQL queries, as described in R. Kimbell, "The Data Warehouse Toolkit," John Wiley and Sons, 1996. However, this suffers from the same drawbacks as described above.
Another approach to automated navigation between cubes is to employ a drill-through operation. With drill-through, a source cell is associated with one or more target cells. This is either done by specifying an explicit association or by specifying a program that computes the association dynamically. The former is poorly suited to dynamic environments in which new cubes are added and others are deleted or their structure is modified. The latter provides a mechanism for dealing with these dynamics, but in and of itself, providing programmatic control does not solve the problems associated with determining which target data to navigate to.
A third way of organizing data is as text documents. Here, the navigation is from keywords (e.g., as specified in an Internet search engine) or whole documents to other documents. This is accomplished by preprocessing the documents to extract keywords (and keyword sequences) to provide an index. Such an approach works well for fairly static data since the index structures are computed infrequently. However, it works poorly for dynamic data.
Further, it is known that data structured as comma-separated-values (or other separators) can readily be treated as relationally structured data. This is accomplished by: (a) describing each column in terms of the distribution of its element values, and (b) using a similarity metric to find comparable columns in other datasets.
Still further, existing art on federated and multidatabase systems, as described in M. T. Ozsu and P. Valduriez, "Principles of Distributed Database Systems," Prentice Hall, 1991, as well as schema integration, as described in J. A. Larson, S. B. Navathe, R. Elmasri, "A Theory of Attribute Equivalence in Databases with Application to Schema Integration," IEEE Transactions on Software Engineering, vol. 15, no. 4, April 1989, teaches how to address problems with heterogeneous names and semantics of columns in relational databases. These approaches allow for performing SQL queries in which the tables referenced in the from clause may have different relational schema. However, these approaches do not teach how to automate the selection of relational tables to use in the from clause, nor do they address how to determine the target element collection that is closest to the source element collection.
The present invention provides systems and methods that aid in decision support applications by automatically selecting data relevant to an analysis. This is accomplished by using the structure of the source dataset in combination with the content of the source element collection to identify the closest element collections within one or more target datasets.
Particularly, the invention is implemented in a form which includes certain functional components. These components, as will be explained, may be implemented as one or more software modules on one or more computer systems. A first component is referred to as an inter-dataset navigation engine (IDNE). The IDNE is invoked by analysis applications to automate the selection of related data. The IDNE makes use of another component referred to as dataset access services. The dataset access services component knows the accessible datasets and their structures, creates and manipulates collection descriptors, and provides access to elements within a dataset that are specified by a collection descriptor.
In one embodiment, automated navigation according to the invention may be accomplished in the following manner. First, the IDNE iterates across all target datasets to do the following: (a) use the structure of the source and target datasets to transform the source collection descriptor into a preliminary collection descriptor for the subset of the target dataset that is closest to the source element collection; (b) construct the final collection descriptor by transforming the preliminary collection descriptor until it specifies a non-null subset of the target dataset; and (c) compute a distance metric representing how close the source element collection (or collection descriptor) is to the target element collection (or collection descriptor). The IDNE then returns a list of triples including a name of the target dataset, a target collection descriptor, and a value of the distance metric for each target dataset.
The list may be presented to an end-user who then selects the preferred target dataset. Alternatively, the list may be provided to a program that does additional processing. The list may be sorted by descending value of the distance metric so as to provide a ranking of the target datasets and their target element collections.
It is to be appreciated that the systems and methodology of the present invention advantageously eliminate the drawbacks (e.g., accuracy, comprehensiveness, etc.) that exist in the manual navigation approach and other prior art approaches described above. To illustrate the operation of this methodology, we apply it in the context of the previously presented exemplary scenario. Recall that the collection descriptor of the elements in the source dataset is: shift=1, hour=8, subnet=9.2.15, division=25, department=MVXD, user=ABC, and transaction=_XX. We also use the operating system (OS) data previously introduced. This data is structured into the following columns: shift, hour, minute, subnet, division, department, efficiency, waiting time, CPU waits, I/O waits, page waits, and CPU execution times.
The invention performs steps (a) through (c) above as follows. A preliminary collection descriptor is constructed for the OS data by transforming the source collection descriptor (step (a)). In particular, the constraints such as transaction=_XX that have an attribute that is not present in the target dataset are addressed. One approach to resolving this is to remove such constraints. Doing so yields: shift=1, hour=8, subnet=9.2.15, division=25, department=MVXD, and user=ABC.
Next, the final collection descriptor in the target dataset is constructed (step (b)). This can be achieved by doing the following. First, the element collection specified by the preliminary collection descriptor is retrieved. If this collection is empty, one or more constraints from the collection descriptor are removed. This is repeated until a non-null element collection is obtained. In the exemplary scenario, there is no data for user ABC. So, the constraint user=ABC is removed. Thus, the final target collection descriptor is shift=1, hour=8, subnet=9.2.15, division=25, and department=MVXD.
Lastly, the metric for the distance between the source and target element collections is computed (step (c)). Note that in the above construction, the target collection descriptor always has a subset of the constraints in the source collection descriptor. Thus, a convenient distance metric is the difference between the number of constraints in the source and target collection descriptors. In the exemplary scenario, this value is two.
Accordingly, the present invention provides automation for selecting datasets relevant to analysis tasks. Such automation is crucial to improving the productivity of decision support in systems management applications. The automation enabled by the invention provides value in many ways. For example, the invention makes the novice analyst more expert by providing a list of target datasets and collection descriptors that are closest to an element collection at hand (i.e., the source element collection). As a result, the novice focuses on the datasets that are most likely to be of interest in the analysis task. By way of further example, the invention makes expert analysis more productive. This is achieved by providing the target collection descriptor for each target dataset thereby enabling the construction of a system in which analysts need only click on a target dataset (or collection descriptor) in order to navigate to its associated element collection.
The techniques employed today for dataset selection (e.g., drill-through in MDDBs) embed fixed associations between datasets or require special purpose programs that must be maintained if datasets and/or attributes are added or deleted. In contrast, the invention uses the structure of the data itself to select relevant data. Such an approach adapts automatically to changes in the structure and content of the data being analyzed.
In another embodiment of the invention, the set of attributes considered when transforming the source collection descriptor into the target collection descriptor may be constrained. Indeed, the exemplary scenario does not consider the attributes response time, efficiency, waiting time, CPU waits, I/O waits, page waits, and CPU execution time.
In yet another embodiment of the invention, different levels of importance may be assigned to attributes. For example, a match on the attribute subnet may be considered more important than a match on the attribute division. This may be implemented by changing the manner in which the distance metric is computed so that it includes weights assigned. In this way, differences in the values of more important attributes result in larger distances than do differences in less important attributes.
Automated navigation according to the invention can be applied in many domains. For example, in analysis of manufacturing lines, measurement datasets exist for machines in the manufacturing line as well as for the interconnection of these machines. Automated navigation according to the invention can aid with decision support for scheduling and planning based on this data. By way of a further example, in transportation systems, datasets exist for measurements taken by road sensors and traffic reports. Automated navigation according to the invention can aid in planning highway capacity over the entire network of roadways. It is to be understood that the above applications are merely exemplary in nature and not intended to limit the applicability of the invention. Furthermore, it is to be appreciated that automated navigation according to the invention can be accomplished centrally at a server or in a distributed manner amongst several smaller server machines.
These and other objects, features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.
FIG. 1A is a block diagram illustrating a system for implementing an automated navigation system according to an exemplary embodiment of the invention;
FIG. 1B is a block diagram illustrating a hardware implementation for both an end-user computer system and an analysis server computer system according to the invention;
FIG. 2 is a flow diagram illustrating an automated navigation method according to an exemplary embodiment of the invention;
FIG. 3 is a flow diagram illustrating a technique for computing a target collection descriptor that best matches a source collection descriptor according to an exemplary embodiment of the invention;
FIG. 4 is a flow diagram illustrating a technique for computing a distance metric according to an exemplary embodiment of the invention; and
FIG. 5 is a diagram illustrating a graphical user interface for presenting exemplary results associated with automatic navigation according to an exemplary embodiment of the invention.
It is to be appreciated that the term "processor" as used herein is intended to include any processing device, such as, for example, one that includes a CPU (central processing unit). The term "memory" as used herein is intended to include memory associated with a processor or CPU, such as, for example, RAM, ROM, a fixed memory device (e.g., hard drive), a removable memory device (e.g., diskette), etc. In addition, the term "input/output devices" or "I/O devices" as used herein is intended to include, for example, one or more input devices, e.g., keyboard, for inputting data to the processing unit, and/or one or more output devices, e.g., CRT display and/or printer, for providing results associated with the processing unit. It is also to be understood that various elements associated with a processor may be shared by other processors. Accordingly, software components including instructions or code for performing the methodologies of the invention, as described herein, may be stored in one or more of the associated memory devices (e.g., ROM, fixed or removable memory) and, when ready to be utilized, loaded in part or in whole (e.g., into RAM) and executed by a CPU.
Referring to FIG. 1A, a block diagram is shown of a system for implementing an automated navigation system according to an exemplary embodiment of the invention. As shown, an end-user computer system 102 is coupled to one or more analysis server computer systems 104-1 through 104-K. Each analysis server includes one or more analysis applications 110-1 through 110-N with which the end-user computer system interfaces. The analysis applications are analysis programs relating to various applications for which analysis of datasets is required, e.g., manufacturing lines, transportation systems, etc. The analysis applications use a service interface component 120 to access datasets associated with a dataset access service component 130. The analysis applications also use the service interface component 120 to access the services of an inter-dataset navigation engine (IDNE) 140. It is to be appreciated that the services interface component 120 is employed as an interface between the analysis applications and the dataset access service component 130 and the IDNE 140 since the various analysis applications may have different communication formats. In this manner, the dataset access service component and the IDNE do not have to handle a large variety of communication formats when communicating with the analysis applications, but rather, they communicate with the services interface 120 which preferably converts the various formats to a single format understood by the dataset access service and IDNE. The services interface component 120 may also serve as a buffer for the dataset access service and the IDNE. The particular implementation of the services interface component is not critical to the invention since, given the design choices for the communication formats of the analysis applications, the dataset access engine and the IDNE, implementation of the interface is a matter of programming choice within the skill level of the ordinary artisan.
The IDNE 140, as will be explained in detail below, provides automation for selecting target element collections that are close to a source element collection. To accomplish this, the IDNE has access to an externally specified description of attribute weights 145 that provides a means to associate a positive value with each attribute of every dataset accessed through a dataset access engine 150. The IDNE makes use of the dataset access service 130. Within the dataset access service 130 is the dataset access engine 150 and one or more datasets 160-1 through 160-M. It is to be appreciated that the dataset access engine 150 may be implemented in many ways. For data that is organized as relational tables, engine 150 is implemented as a relational database engine. For data that is organized as a multidimensional database, engine 150 is implemented as a MDDB engine. Further, the dataset access engine 150 may be a combination of several conventionally available data access engines. The engine and its datasets may be local to a single computer or may be distributed across a plurality of computers, e.g., using a client-server protocol.
Referring now to FIG. 1B, a block diagram is shown of a hardware implementation for both an end-user computer system 102 and an analysis server computer system (e.g., 104-1 through 104-K) for implementing the invention. Each computer system includes a processor 170 coupled to a memory 180 and I/O device(s) 190. In the case of an analysis server, the processor 170 performs the functions associated with the various components running therein. The memory 180 is used by the processor for performing such functions and for storing the datasets and results of the processes. The I/O devices may include one or more data input devices (e.g., keyboard, etc.) for inputting data, if needed, and/or one or more data output devices (e.g., display) for presenting a user with results associated with the functions performed in accordance with the various components. For example, a display may present a user with a graphical user interface for viewing such results. As indicated, both the end-user computer system and analysis computer system may have such a hardware architecture. Also, it is to be appreciated that each analysis server may be in communication with the end-user and each other analysis server via conventional communication links, e.g., local area network, wide area network, etc. Further, as mentioned, more than one computer system may be employed to implement the components illustrated in any one analysis server.
Referring to FIG. 2, a flow diagram is shown of an automated navigation method according to an exemplary embodiment of the invention. It assumed that a user (e.g.,. analyst) at the end-user computer system 102 calls upon at least one of the analysis application programs (110-1 through 110-N) running on at least one of the analysis servers (104-1 through 104-K) to assist in a particular analysis task, e.g., the long response time example detailed above. In such, case, the analysis application communicates with the IDNE 140 and dataset access services 130 such that these components can provide the user with automated navigation between datasets containing dynamic data and having dissimilar structures in order to complete the particular analysis task.
Accordingly, in step 200, the IDNE is provided with a source collection descriptor (SCD) from an analysis application. The source collection descriptor may have been identified by the user at the end-user system or generated by the analysis application. Similarly, in step 210, the IDNE is provided with a list of target datasets (LTDS) from an analysis application. Again, these may also be specified by the user or the analysis application. The IDNE then obtains the listed target datasets from the dataset access engine 150. It is to be understood that the dataset access engine 150 retrieves these datasets from the appropriate memory locations where such datasets are stored. Then, in step 220, the IDNE considers each target dataset within this list in turn. TDS denotes a target dataset in the LTDS. In step 230, a collection descriptor in TDS is obtained such that the collection descriptor is closest to (best matches) the SCD input to the IDNE in step 200. In step 240, the IDNE computes the distance (DIST) between the source and target element collections. Then, in step 250, the name of the target dataset, the target collection descriptor, and the computed distance are saved in an OUTPUTLIST.
The method then returns to step 220 where it is determined whether all the target datasets have been processed. If they have not, then steps 230, 240 and 250 are repeated for each TDS in turn. If all TDSs have been processed, the IDNE proceeds to step 260. In step 260, OUTPUTLIST which contains the triplet set of information for each TDS processed is sorted by DIST. That is, each target collection descriptor generated may be ranked in a list with the target collection descriptor resulting in the smallest distance metric with respect to the source collection descriptor appearing at the top of the list and target collection descriptors resulting in increasingly larger distance metrics with respect to the source collection descriptor appearing correspondingly lower in the list. The OUTPUTLIST is then returned by the IDNE to the analysis application, which then provides the list to the end-user 102 for display to the user.
Referring now to FIG. 3, an exemplary technique is shown for computing a target collection descriptor that best matches a source collection descriptor, e.g., step 230 of FIG. 2. In step 310, a preliminary target collection descriptor (PTCD) is constructed for the current target dataset being considered by appropriately transforming the source collection descriptor. For datasets organized as relational tables, this can be accomplished in the manner specified by the following pseudo-code:
pTCD = Default collection descriptor for TDS
Do for each constraint in SCD (which is the SQL where
clause used in the SCD)
If the attribute of the constraint is in the schema of TDS
add the constraint to pTCD
For datasets organized as multidimensional databases, step 310 may be accomplished in the manner specified by the following pseudo-code:
Do for each dimension sd in the source dataset
td = dimension in TDS that's closest to sd
(e.g., most attributes in common)
scv = vector of SCD constraints for dimension sd
tcv = vector of constraints in scv for which the
attributes are in TDS
Set dimension td of pTCD to tcv
In step 320, the final target collection descriptor is computed. For relationally organized data, this can be accomplished in the manner specified by the following pseudo-code:
TCD = pTCD
Do until the element collection specified by TCD is empty
For each constraint in TCD
TCD′ = TCD − {constraint}
If the element collection of TCD′ is not empty
Leave the do-loop
Choose a constraint in TCD and delete it
This code removes constraints from the preliminary collection descriptor until a non-empty element collection is obtained. To perform step 320 for data organized into a multidimensional database, the following pseudo-code may be employed:
TCD = pTCD
Do until the element collection specified by TCD is not empty
For each dimension td in TCD
TCD′ = drill-up of TCD
If the element collection of TCD′ is not empty
Leave the do-loop
TCD = drill-up on all dimensions of TCD
Here, constraints are removed in a manner that preserves the multidimensional structure of the data by performing drill-ups on dimensions until a non-null element collection is obtained.
Referring now to FIG. 4, an exemplary technique is shown for computing a distance metric between a source element collection and a target element collection, e.g., step 240 of FIG. 2. In step 410, the set of constraints in the source collection descriptor (Count_S) is determined, and the weights of associated attributes (as specified in 145 of FIG. 1A) are added. Similarly, in step 420, the set of constraints in the target collection descriptor 20 (Count_D) is determined, and the weights of associated attributes (as specified in 145 of FIG. 1A) are added. Then, in step 430, the difference between the two numbers is computed as the distance metric DIST. Also, it is to be appreciated that the distance metric may alternatively represent a difference in the respective number of constraints in the source collection descriptor and the target collection descriptor.
Thus, as mentioned above, constraints may be assigned weights such that a particular constraint is considered more important than another constraint. For instance, if it is determined that constraint A is more important than constraint B, then constraint A is assigned a larger weighting value than constraint B. Then, during respective computations of distance metrics between a source collection descriptor containing constraints A and B and a first target collection descriptor differing only by the absence of constraint A and a second target collection descriptor differing only by the absence of constraint B, the distance metric for the first target collection descriptor will be larger than the distance metric for the second target collection descriptor. This is due to the heavier weighting, that is, greater importance, of constraint A. Therefore, a target collection descriptor that contains constraint A but not constraint B is considered a better match with the source collection descriptor containing constraints A and B than a target collection descriptor that contains constraint B but not constraint A.
Referring to FIG. 5, an example is shown of a graphical user interface 500 that displays the results produced by the invention. It is to be appreciated that this is preferably presented to the user (e.g., analyst) on a display associated with the end-user system 102. The information presented includes the dataset name (e.g., MVS Monitor 3, UNIX Accounting, Financials, etc.), the target collection descriptor formed for each target dataset considered, and the value of the distance metric computed for each target collection descriptor. Note that the list is sorted in descending order by value of the distance metric. Doing so helps the user to select the target collection descriptor and dataset that are most appropriate for the analysis at hand.
Although illustrative embodiments of the present invention have been described herein with reference to the accompanying drawings, it is to be understood that the invention is not limited to those precise embodiments, and that various other changes and modifications may be affected therein by one skilled in the art without departing from the scope or spirit of the invention.
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|Method and system for combining pattern matching and optimization
|Oct 7, 1998
|Sep 11, 2001
|International Business Machines Corporation
|System and method for similarity searching in high-dimensional data space
|Feb 24, 1998
|Sep 25, 2001
|At&T Corp.
|System and method for accessing heterogeneous databases
|Feb 10, 1999
|Dec 11, 2001
|International Business Machines Corporation
|System and method for automated problem isolation in systems with measurements structured as a multidimensional database
|Apr 15, 1996
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|Clive M Fourman
|Computer network
|Dec 21, 1999
|Jan 14, 2003
|Lucent Technologies Inc.
|Histogram-based approximation of set-valued query-answers
|Apr 14, 2000
|Sep 30, 2003
|Douglas K. Keith
|Displaying implicit associations among items in loosely-structured data sets
|Aug 30, 1999
|Oct 21, 2003
|Morphism, Llc
|Method and apparatus for representing and navigating search results
|FastMap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia datasets-Christos Faloutsos and King-Ip (david) Lin-1995-ACM (pps: 163-174).*
|FastMap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia datasets—Christos Faloutsos and King-Ip (david) Lin—1995—ACM (pps: 163-174).*
|M.T. Ozsu and P. Valduriez, "Principles of Distributed Database Systems," Prentice Hall, pp. 78-89, 1991.
|R. Kimbell, "The Data Warehouse Toolkit," John Wiley and Sons, pp. 1-19, 1996.
|R.F. Berry and J.L. Hellerstein, "A Flexible and Scalable Approach to Navigating Measurement Data in Performance Management Applications," Second IEEE Conference on Systems Management, Toronto, Canada, pp. 1-12, Jun., 1996.
|S.B. Navathe, R. Elmasri, "A Theory of Attribute Equivalence in Databases with Application to Schema Integration," IEEE Transactions on Software Engineering, vol. 15, No. 4, pp. 449-463, Apr. 1989.
|Citing Patent
|Filing date
|Publication date
|May 23, 2003
|Apr 29, 2008
|Oracle International Corporation
|Registration of solved cubes within a relational database management system
|Dec 12, 2008
|Apr 17, 2012
|At&T Intellectual Property I, L.P.
|Methods and apparatus to determine statistical dominance point descriptors for multidimensional data
|Jul 8, 2008
|May 1, 2012
|Datavine Research Services
|Method and apparatus for access, integration, and analysis of heterogeneous data sources via the manipulation of metadata objects
|May 31, 2008
|Sep 9, 2014
|Red Hat, Inc.
|ETL tool utilizing dimension trees
|Apr 25, 2003
|Oct 21, 2014
|Oracle International Corporation
|Using relational structures to create and support a cube within a relational database system
|Aug 29, 2008
|Oct 28, 2014
|Red Hat, Inc.
|Real time datamining
|Nov 30, 2008
|Dec 16, 2014
|Red Hat, Inc.
|Forests of dimension trees
|Mar 27, 2014
|Aug 30, 2016
|Hitachi, Ltd.
|Resource management method and resource management system
|Aug 12, 2002
|Jul 3, 2003
|O'halloran Sharyn
|Method and apparatus for access, integration, and analysis of heterogeneous data sources via the manipulation of metadata objects
|May 23, 2003
|Nov 6, 2003
|Oracle International Corporation
|Registration of solved cubes within a relational database management system
|Oct 14, 2003
|Apr 14, 2005
|Stokke Michael A.
|System and method facilitating automated navigation for user interface(s)
|Feb 11, 2005
|Jul 14, 2005
|O'halloran Sharyn
|Method and apparatus for access, integration, and analysis of heterogeneous data sources via the manipulation of metadata objects
|Jul 8, 2008
|Oct 30, 2008
|David Epstein
|Method and Apparatus for Access, Integration, and Analysis of Heterogeneous Data Sources Via the Manipulation of Metadata Objects
|Jun 28, 2007
|Jan 1, 2009
|Microsoft Corporation
|Automated model generator
|May 31, 2008
|Dec 3, 2009
|Williamson Eric J
|ETL tool utilizing dimension trees
|Aug 29, 2008
|Mar 4, 2010
|Williamson Eric J
|Real time datamining
|Nov 30, 2008
|Jun 3, 2010
|Williamson Eric J
|Forests of dimension trees
|Dec 12, 2008
|Jun 17, 2010
|Graham Cormode
|Methods and Apparatus to Determine Statistical Dominance Point Descriptors for Multidimensional Data
|Aug 21, 2014
|Dec 17, 2014
|Hive-based network alarm information OLAP method
|U.S. Classification
|1/1, 707/999.003, 707/999.01, 707/999.101, 707/999.102
|International Classification
|Cooperative Classification
|European Classification
|May 7, 1999
Owner name: INTERNATIONAL BUSINESS MACHINES CORPORATION, NEW Y
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:HELLERSTEIN, JOSEPH L.;REEL/FRAME:009946/0742
Effective date: 19990430
|May 23, 2006
|Certificate of correction
|Jul 13, 2007
|Fee payment
Year of fee payment: 4
|Jul 15, 2011
|Fee payment
Year of fee payment: 8
|Oct 16, 2015
|Maintenance fee reminder mailed
|Mar 9, 2016
|Lapse for failure to pay maintenance fees
|Apr 26, 2016
|Expired due to failure to pay maintenance fee
Effective date: 20160309


text:
If I take a Lortab how long do I have to wait to pass a drug test?
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subset: commoncrawl


text:
December 25th 1871 --- April 14th 1915
Skriabin, Alexander Nikolaievich (1872-1915), Russian pianist and composer; born Moscow, where he studied under Taneiev and Safonov at the Conservatory.
He toured Western Europe as a pianist. Among his works are 3 symphonies, 2 tone-poems (L'Extase and Prometheus), numerous pianoforte sonatas, preludes, etudes, valses, etc. In Prometheus he attempted to synthesise music and colour by means of a 'colour-keyboard', but without success; he developed, too, an individual harmonic basis which had a symbolic musical significance for him, comparable with the mysticism of the theosophists.
The device, demanding special study, failed to attract the general concert-going public. His work has had some influence on Russian music.
Last Updated on 7th April 2001
By Steven
And now for the Music
(2094)"Mazurka opus.25" Sequenced by Serge Winitzki?
(2095)"Mazurka opus.25, No.3" Sequenced by Serge Winitzki?
(2096)"Mazurka opus.25, No.9" Sequenced by Serge Winitzki
(2097)"Mazurka opus.30, No.4" Sequenced by Serge Winitzki?
(2098)"Mazurka opus.33, No.2" Sequenced by Serge Winitzki?
(2099)"Mazurka opus.33, No4" Sequenced by Serge Winitzki?
(2092)Flammes Sombres Opus.73, No.1 (Info kindly supplied by Gary K Allen) Sequenced by ?
(47)Flammes Sombres Opus.73, No.2 (Info kindly supplied by Gary K Allen) Sequenced by ?
(2091) Prelude Opus,11, No.11 Sequenced by Robert Finley?
(1084) "Etude Op 2 No 1", Sequenced by Robert Finley
(1082) "Etude Opus 8 No 12", Sequenced by Robert Finley
(1079) "Etude Opus 8 No 5", Sequenced by Robert Finley
(1083) "Etude Opus 8 No.2, Sequenced by ?
(314) Prelude Opus,11, No.1 Sequenced by ?
(315) Prelude Opus,11, No.6 Sequenced by ?
(316) Prelude Opus,11, No.14 Sequenced by ?
(317) Prelude Opus,11, No.15 Sequenced by ?
(312) Prelude Opus,13, No.2 Sequenced by ?
(313) Prelude Opus,13, No.3 Sequenced by ?
(1081) "Prelude Opus,42 No.5", Sequenced by Robert Finley
(1080) "Prelude Opus,42 No.4", Sequenced by Robert Finley
(1085) "Mazurka opus.1", Sequenced by ?
(1086) "Mazurka opus.2", Sequenced by ?
(1087) "Mazurka opus.3", Sequenced by ?
(1088) "Mazurka opus.5", Sequenced by ?
(1089) "Mazurka opus.17", Sequenced by ?
(1090) "Mazurka opus.21", Sequenced by ?
(1091) "Mazurka opus.24 No.1", Sequenced by ?
(1092) "Mazurka opus.24 No.2", Sequenced by ?
(1093) "Mazurka opus.24 No.3", Sequenced by ?
(1094) "Mazurka opus.24 No.4", Sequenced by ?
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Move the cursor over each thumbnail image (or tap on it) to view it in the larger window.
All projects, whether new or renovation are planned and designed using our computer aided design system. This allows us to prepare a rendering of your new kitchen for you to review and approve before any construction is started.


text:
Joint Action
December 2008
Keeping People Mobile
BOA CELEBRATES ITS 90th BIRTHDAY HAPPY 90th BIRTHDAY BOA! The BOA was established by Sir Robert Jones (1857-1933) in 1918 and his life story makes interesting reading. As with all men, the career of Robert Jones was the result of a combination of circumstances and personal qualities which had never occurred before and never will again. Robert Jones was born in Rhyl, North Wales on 28th June 1857. When he was 5 years old his father gave up a lucrative career as an architect and moved to London to become a journalist, consequently the family became quite poor. Robert was educated at Sydenham College, and aged 16 left London aided by an uncle, Hugh Owen Thomas, who persuaded him to get a medical education. Uncle Hugh was an eccentric genius, but an exceptionally well trained and successful orthopaedist in Liverpool, the son of a celebrated bone-setter. After his graduation in medicine, young Jones practised with his uncle for a time and then set out for himself. Robert Jones became committed to improving orthopaedic care and fostering the specialisation of orthopaedics, and with great foresight, he helped found the British Orthopaedic Association (BOA). His position as director of orthopaedics in the military meant that from then on, all orthopaedists were helped by him through this new association.
its purpose as working for the benefit of patients by continuing the advance of science and practice of Trauma and Orthopaedic Surgery that Jones had started. In fact, the BOA has been so successful in this role that at present, 40% of all surgeons in the UK work in Trauma and Orthopaedics. Sir Robert Jones died on 14th January in 1933 at the age of 76 in Bodynfoel, near Llanfechain, the home of his daughter in Wales. 'In view of the great services rendered by him to humanity at large,' his ashes were laid to rest in Liverpool Cathedral by 1935, England had 40 hospitals with a total of 6000 orthopaedic beds and 400 orthopaedic clinics.
Today, with over 4,300 members, the association still sees
In December 2008, the BOA held a reception to mark its 90th anniversary and celebrate the foresight of Sir Robert Jones. Please will you join with us by making your own birthday gift to help fund the next 90 years of the British Orthopaedic Association. You can do this by using the response coupon enclosed with this newsletter and returning it to us in the envelope provided.
My Joint Account is a key area of the new website that allows you to interact with Joint Action and other members. Members will be able to update and add to their personal details. Members of Joint Action will be able to apply for an Orthocard. An Orthocard holder can apply for a replacement card, or cancel their Orthocard or Joint Account membership. They will also be able to opt out of recieving fundraising or marketing based communications. People who have donated can see when and what they have donated. Also, if donations have been identified as being associated with a particular appeal or research activity then, this will be highlighted. From now on, donors will be able to see the history of their donations and the impact they are making in Orthopaedic research in the UK.
As a previous supporter you have access to your own private area of the new website, known as "My Joint Account"
Additionally, there are links to the latest news, appeals and events pages. For more details of all the facilities the new website has to offer, log on to www.jointaction.org.uk
A new inovation to help ease your travel after an implant The British Ort This is the Joint Action Membership Card that offers people with implants numerous benefits.
Keeping Pe Reg. Charity
Or th op ae
di c Im pl an
ople Mobil No. 106699
t Co nf irm
at io n Ca
• Peace of mind and ease of transit at Security Checkpoints • Helps reduce embarrassment if the alarm goes off • Evidence and verification of a replacement joint • Gives important information to medics in the case of an accident • Provides vital information for Dentists and Doctors to help prevent the serious risk of infection
Team BOA: The 2009 Flora London Marathon
As a fundraising event, there is no marathon in the world
Feel the difference
that comes close to the Flora London Marathon.
With the Walking the way to Health group, you can help speed recovery from orthopaedic intervention and improve general fitness.
Joint Action is again proud to take part in the this year's marathon on Sunday 26th April 2009, to help raise the much needed sponsorship monies to fund Orthopaedic Research in the UK. We would like to take this opportunity to introduce you to the team which is made up of runners from across all walks of life and in all parts of our country.
Mr Mark Bowditch, from Woodbridge, Suffolk
Walking can...
Mr Adam Brooks, from Swindon
Make you feel good
Mr Aroonkumar Chouhan, from Walsall
Give you more energy
Ms Becky Hall, from the Journal of Bone and Joint Surgery (London)
Reduce stress and help you sleep better Keep your heart 'strong' and reduce blood pressure Help to manage your weight Tone muscles after joint implants
Why is walking the perfect activity for health? Almost everyone can do it You can do it anywhere and any time It's a chance to make new friends
Mrs Jane Leslie, from Easter Compton, Bristol Ms Reena Minhas, from Ilford Mr James R Barnes, from Bristol Mr Andrew Sprowson, from Newcastle Mr Andrew Terry, from Bury St Edmunds Mr Alex Trompeter, from Guildford Each member of the team has agreed, graciously, to raise a minimum of £1,000 , so anything you can do to help them
It's free and you don't need special equipment
on their way would be greatly appreciated. If you wish to
You can start slowly and build up gently
donate online to any of their causes, please visit www.justgiving.com/boa and follow the instructions
To find out more, and how you can partake in organised walks with Natural England, tick the appropriate box on the enclosed response form.
provided or complete the relevant part of the enclosed response coupon, naming your favoured runner if you wish.
RESEARCH PROJECTS UPDATE Where our funds are spent? Joint Action relies entirely on voluntary contributions to fund our work. This very nature of our funding, leads us to take the responsibility of investing the donations we receive very seriously indeed. Our primary focus is to raise and distribute the funds that improve patient care and the treatment of diseases of, and injuries to, the musculoskeletal system by funding high quality research in the UK. We are the only UK charity that specialises in supporting and raising funds for all areas of Orthopaedic research. We believe we are uniquely placed to ensure that the maximum impact is made of every donation received by being underpinned by The British Orthopaedic Association, who represent more than 4,300 UK orthopaedic surgeons and researchers and who form the pool of professionals that we draw the world leaders from to stand on our Research and Grants Committees. Our Research Committee, following extensive discussions with affiliated bodies such as the British Orthopaedic Research Society, The American Academy of Orthopaedic Surgeons, The British Geriatrics Society, The International Society for Fracture Repair, The National Osteoporosis Society and The International Osteoporosis Foundation alongside other relevant bodies have developed and published a Research Agenda in July 2005. Copies can be viewed or requested from www.jointaction.org.uk. Research topics covering... 1. Arthritis and Arthroplasty 2. Soft Tissue Injuries 3. Major Limb Trauma 4. Osteoporosis and Fragility Fractures 5. Spinal Disorders 6. Paediatric Orthopaedics This agenda maps out the direction for orthopaedic research both nationally and internationally. By focusing on certain priority areas of need, The British Orthopaedic Association, through its fundraising function, Joint Action is able to achieve maximum potential from its research grants for the benefit of orthopaedic patients throughout the country. Within this Agenda, our Grants Committee then takes part in an annual review of the many research applications we receive. We then undertake an independent peer review to ensure that we only fund the highest quality projects that
will most effectively drive forward the medical advances to make the most impact on ordinary people's lives. Y ou can read about three examples of our research below, or visit our website at www.jointaction.org.uk to learn about other areas we fund. You can also register your interest to be kept informed about the latest developments in these projects as they occur by opening your very own Joint Account.
Outcome with gait analysis of limb reconstruction for severe forms of fibular hemimelia Simon Thomas, Nuffield Orthopaedic Centre, Oxford £5,000 Fibular hemimelia is a birth defect in which there is partial or complete absence of the fibular bone together with a variable amount of lower limb shortening, knee and ankle instability and often longitudinal loss of foot bones. It is the commonest deficiency of long bones, affecting about 1 in 25,000 live births. For children with severe involvement, amputation of the foot and prosthesis fitting is often recommended to equalise limb lengths, facilitate walking and minimise time spent in hospital as a child. This study investigated the use of an external frame to lengthen the involved bones, stabilize and reconstruct affected joints and avoid lower limb amputation. Outcomes were assessed into adulthood with limb specific and general health status questionnaires and 3D gait analysis. The results confirmed that children treated with this reconstructive surgery had good functional outcomes and generally good quality of life. They walked more slowly than a comparable group of amputees but with a similar degree of efficiency when looking specifically at energy consumption. These results are of fundamental importance for orthopaedic surgeons worldwide when treating children with this severe birth defect. They add objective data to the argument over limb reconstruction versus amputation and prosthetic use.
Driving Time After Shoulder Surgery Andrew Wallace, Imperial College, London £22,000 Going back to driving is often the most frequent question asked after any surgery, particularly shoulder surgery, and, is often directly related to the livelihood of the patients. Driving is considered an essential skill to acquire and maintain throughout life. It needs complicated coordination skills involving various sensory and motor skills. Whilst past studies have investigated mental, visual and auditory components of driving, no research has been done on the upper limb, especially the shoulder joint involvement in driving.
The essential components of driving involve turning steering wheels, changing gears in manual cars and application of the hand brake. Little is known about the role of the shoulder in these movements and how orthopaedic problems affect movement, and how patients can be assessed to see if they can safely return to driving. This project is investigating when a patient can safely resume driving after four common types of shoulder surgery.
New Minimally Invasive Orthopaedic Surgery for Children with Cerebral Palsy Tim Theologis, Nuffield Orthopaedic Centre, Oxford £61,000 Cerebral palsy is the most common serious physical disability in childhood. Children with diplegic type of cerebral palsy, mainly affecting both legs, often experience significant walking difficulties. These difficulties usually deteriorate over time due to the development of joint deformities and increasing height and weight. Both the deformities and walking difficulties can be greatly improved with orthopaedic surgery, which involves lengthening several muscles and realignment of bones, at the same time (multi-level surgery) and very prolonged rehabilitation time. It is important that surgical techniques and post-operative rehabilitation need to improve further to preserve and improve muscle strength and motor function. In this study, Mr Theologis and his team in Oxford, plan to combine new strength preserving surgical techniques which allow earlier mobilisation, with resistance strength training at an earlier stage post-operatively, to provide a 'strength preserving programme' for children with cerebral palsy. He anticipates that this new minimally invasive or 'keyhole' technique of multilevel surgery, combined with intensive post-operative strengthening, will lead to a faster and more optimal recovery time from surgery for children with cerebral palsy, their families and carers. This would be a significant advance on present practice.
neighbours, John Charnley would have been able to witness at firsthand, the positive effect that his surgery had on restoring her quality of life. Unfortunately a subsequent elbow break in her 80's again required orthopaedic surgery, but she kept on going to the last. Before her death, Mrs. Dorothy Latta made arrangements for a generous legacy to be left to orthopaedic research in order to enable current and future generations to receive practical benefit from the underfunded area of limb prosthesis. Following extensive peer review, Joint Action's Grants Committee identified a worthy recipient of The Latta Fellowship to a total of £400,000 from Dorothy's estate, in Mr Eleftherios Tsiridis a Consultant Orthopaedic Surgeon from Leeds General Infirmary. Mr Tsiridis (or Teri as he is fondly known) has designed a five year study that brings together multi-disciplinary specialists Professor Z. Jin, Dr R. Wilcox and Dr A. Jones from the department of Bioengineering at Leeds University to optimise the treatment of fractures in the femurs following hip replacement surgery. These fractures in the femur (thigh bone) are a relatively common complication that occurs during or after hip replacement surgery. The fixation of such fractures is technically difficult, and it is important that the treatment is optimum to ensure that the fracture heals and further damage does not occur. This study will use computer models to compare the many different treatment methods that are currently used. The computer models will allow the research team to examine the loading (weight put through the bone) and movement in the fracture for each different method. This Latta Fellowship is expected to culminate in providing valuable information that will have a direct benefit to orthopaedic patients requiring surgery for hip fracture. This work would not have been possible without the foresight and generosity of Mrs. Dorothy Latta, for which we, and future generations of orthopaedic patients, can be very grateful. If you wish to discuss ways that you could also make a lasting impact on an underfunded area of research, perhaps in memory of a loved-one, then please contact Julia Smith at Joint Action, in the strictest confidence, on 020 7405 6507.
Joint Action appeal is raising funds to improve patient care and the t: 0114The280 3200
We are delighted and honoured to announce Joint Action's largest ever orthopaedic research Fellowship, The Latta Fellowship, which has been made possible by the vision of a remarkable woman called Mrs. Dorothy Latta. Mrs. Dorothy Latta passed away 2nd April 2006 at the grand old age of 93 following a very active and full life. She benefited from one of the first Charnley hip replacements in the late 1960's and as one of his next-door
treatment of diseases of, and injuries to, the musculoskeletal system by funding high quality research in the UK, as outlined in our Research Agenda. If you wish to be a part of this research by helping to fund it, please complete the relevant sections of the response coupon.
Keeping People Mobile
British Orthopedic Association
Published on Dec 12, 2008
This newsletter focuses on the British Orthopaedic Association's 90th birthday, the Orthocard, our 2009 London Marathon runners, our Walk Mo...


text:
Please confirm that you want to add Learn 3D Modeling: Blender Basics in Under 2 Hours to your Wishlist.
Learn the basic tools, shortcuts, and important aspects of the popular 3D modeling software, Blender, without any prior knowledge!
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Course Overview
This course will cover the very basics for those that have never used the Blender software before. Students who have never used the Blender software before will feel a lot more comfortable with it by taking this course. After learning some new concepts, you will test your knowledge by creating some basic models.
At the end of this course, you should have the confidence to go and further your knowledge in the Blender software. This course was designed with complete beginners in mind, so you should be able to create very basic 3D models and feel more comfortable with modeling in the Blender software.
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Learn Blender's interface and make it seem less daunting.
Learn how to select objects.
Learn how to render your scene.
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Learn how to save and export your models into the Unity game engine.
My name is Josh, I'm 19 years old, and I'm a hobbyist in both the Blender and Unity softwares. I'm a Computer Science major by day, and a Udemy instructor by night. Ever since my freshman year of high school, I have developed a passion for 3D modeling and game development after taking a drafting course. I have industry certification in the AutoCad software, but have since moved on and have been using both Blender and Unity for over four years. In my free time, I enjoy traveling, gaming, video editing, mathematics, and of course teaching here on Udemy! I hope that I can spread my knowledge of both the Blender and Unity softwares via my courses in both a fun and efficient manner.


text:
Online Data Modeling Training Syllabus
Part 1: Course Information
Course Description:
This online training course explains in detail about database, data warehouse, data modeling concepts, data modeling types and how these are used in OLTP environments and Data warehouse / Datamart Environments.
To get more information about the program, send an email to email@example.com and we will get back to you.
Course Information:
- Course Name: Advanced Data Modeling Training
- Training Hours: 7.30 AM to 9.00 AM IST
- Days: Tuesdays, Thursday, and Saturday
- Total no. of theoretical classes: 20
- Practical Classes: 5
- Mode of Teaching: Online through SKYPE.
- Instructor: Neelesh & Antony
- Office: USA and Chennai
Course Requirements:
- Internet connection
- Lap Top or Desk Top
- Erwin
- MS Word, MS Excel
- My SQL
- Windows Operating System
Training Material:
- Soft copy of the document will be provided.
Training Certificates:
- Will be provided.
Part 2: Topic Outline/Schedule
Week 01: Data Modeling
- What is a Data Model?
- Who needs Data Modeling?
- Different Data Modeling Tools.
- Explanation for Logical, Physical, Conceptual, Enterprise, and Dimensional Data Model
- Data Modeling Development Life Cycle
- Steps to create a data model
- Data Modeler Role
Week 02: Data Modeling Tools & Database
- Database Commands: Database Objects, Constraints, Create Object, Alter Object, Drop Object, Insert, Delete, Update
- Naming conventions and standards.
- How to create a Logical Data Model?
- How to create a Physical Data Model?
- Comparison of Logical and Physical Data Model
- How to generate SQL Code?
- How to implement it in Database?
- How to create versions of the Data Model?
- How to compare a Data Model with a Database?
- How to create a Data Model from a Database?
- What is Repository?
- What is Metadata?
- How to generate reports?
- What to learn in Data Modeling Tools.
Week 03: Data Warehouse and Data Mart
- What is a Data Warehouse?
- What is a Data Mart?
- What is Snow Flake Modeling?
- What is Star Schema Modeling?
- What is Slowly Changing Dimensions?
- What is a Lookup?
- How to maintain data in Lookups?
- How to send emails to your stake holders?
Week 04: Advanced Topics
- Normalization, Advanced Topics on Data Modeling.
Week 05: Hands on practice with Erwin and MySQL
- Hands on practice with different scenarios.


text:
"My New Haircut, a Jersey Shore satire before there was such a thing back in 2007, is going to be a full-length feature, according to Variety. Zoinks. Very foul language ahead:
Not that the video isn't funny, but…is there really a full movie there? I'd way rather see feature-length adaptations of these other videos…
"Robot Parade," because I'm on a robo-kick today, could easily become a Where the Wild Things Are-style dreamscape about childhood, social conformity, and how to be awesome:
"Last Day Dream" is already a moving self-contained film, but there's obviously a coming-of-age story in here:
I don't see why "An Engineer's Guide to Cats" could involve engineers' guides to many other things:
That crazy slip-n-slide kid is begging for a Superbad-lite Best Summer EVAR kind of teen sex comedy:
What video would you make into a feature, PopWatchers?


text:
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Targeting Secondary Keywords on Your Blog
Jamsi has published a worthwhile post over at workboxers on how he used the Overture tool to find a keyword that was less searched for but also less competitive to target his blog on. He explains it a lot better than I do: 'I noticed that the keyword "Funny vids" ...moreSearch Engine Optimization
Google PR Update Underway
The Google Page Rank update is underway – you can check what your PR will/could be using this Future Page Rank Tool to track what the different data centers have your page rank at. Keep in mind that Page Rank is not the be all and end all – you ...moreSearch Engine Optimization
Google PR Update Imminent?
I've heard from a number of 'interesting' sources that Google will be gearing up for another Page Rank (and maybe a back link) update in the coming fortnight. There are no guarantees on that – but I've heard it from four people now so maybe there is something in the ...moreSearch Engine Optimization
Advice on Moving Blogs to a New Domain
A common problem that many bloggers face is having to work out what to do with a blog that is trapped on a domain that they wish they'd never started it on. An example of this is starting out of a Blogspot or hosted TypePad blog and then realizing that ...moreMiscellaneous Blog Tips
Search Engine Ranking Factors
Steve Rubel points to a useful resource over at seomoz on Search Engine Ranking Factors which is one of the better descriptions of how Search Engines rank pages that I've seen recently. It lists 93 factors to keep in mind. Sounds like a lot to remember – so luckily they've ...moreSearch Engine Optimization
Google Blog Search – First Look Review
My initial reflections on Google's Blog Search: the basic bones of the technology seem to be there. basically it seems to be using similar (or the same) technology as Google News. they seem to be indexing new posts very quickly which is great. the pool of blogs that they are ...moreGeneral
Understanding Google Updates
Matt Cutts (from Google) has an enlightening post on his blog titled – What's an update? which quite simply lays out information on the topic of their updating system. I'm sure it's more complicated than he's outlined here but the information resonates with my experience of Google's updating system as ...moreSearch Engine Optimization
Google Backlink (and Page Rank) Update in Progress
I had a hunch that Google would do an update this weekend and this morning I have logged on to find that an backlink update is under way. I had a feeling they'd use the long weekend to do it. Update: It looks like this could be a Page Rank ...moreSearch Engine Optimization
Dashes vs. underscores in SEO
I regularly get asked whether dashes or underscores in URLs are better for search engine optimization. ie which of the following is best – www.problogger.net/is-this-better or www.problogger.net/is_this_better ? For the past six or so months I've leant towards dashes instead of underscores – but it's always been a 'best guess'. ...moreSearch Engine Optimization


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Mystery Molecules - Starters for KS5 Chemistry
Mystery Molecules
Starters for AS Chemistry
Fifty starter exercises to engage your students. Each activity lasts 5-10 minutes and is provided in PowerPoint format - ideal for use on an interactive whiteboard or projector. Students try to guess the mystery molecule from a series of 5 clues which steadily narrow down the possible options.
- 26 inorganic and 24 organic molecules
- Teacher support document showing rationale behind each clue
- PowerPoint presentation for each Mystery Molecule
- Short clues easy to read out to your class
Debra Leigh, Chemistry teacher
Very good idea - a 'fun' way to get students thinking about several topics. Very concise, yet answering the questions requires knowledge of a wide range of topics.
Chris Workman, Science teacher


text: Valentines Day is almost here! January 24, 2017/0 Comments/in What's New /by Karen McClintockThere is still time to book a limousine or town car for Valentines Day. Let us take care of the driving while you sit back and focus on what really matters, spending quality time with your special someone.


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Magnificent Malmo Day Trip (Product Code: 3589-109828)**See Sweden & Denmark!**
Summary of The Day: Experience two countries in one day and join a small and more personal group in a deluxe minivan with room for only 15 passengers, taking you to the Swedish neighbors of Denmark.
On the way, your driver will tell you stories of the relation between Denmark and Sweden, and you will have a couple of hours to explore Magnificent Malmo and enjoy a beautiful canal boat tour before heading back to Copenhagen. Since the building of the spectacular 'Øresund' Bridge, connecting Denmark to Sweden, the city of Malmo has become one of the most popular destinations for the inhabitants and visitors of the Copenhagen area.
Malmo has just under 295,000 inhabitants, and city life is concentrated primarily around the two squares, Stora Torget and Lilla Torget, where there are many shops and places to eat. With the Swedish currency being so competitive and the wide choice of shops available, Malmo is a great place for shopping and art lovers. You will find a wealth of cultural attractions, including the museum of contemporary and modern art and Moderna Museet Malmo, which is housed in the city's former electricity power station, and which has a string of exhibitions of modern art by appraised artists.
Upon arrival in Malmo, you will be welcomed by our local guide, who will give you information about some of the places to visit. Then, you'll have time to explore the city on your own, and take a beautiful boat tour with Rundan, the Swedish equivalent of Canal Tours from Copenhagen, which is included in your ticket. For an hour, you will enjoy a pleasant tour through canals and under bridges, whilst the guide's narrative will give you a good insight into Swedish history, well-known characters, buildings, and anecdotes. From the small café at Rundan, you will get a basket with coffee and cake to bring with you on the tour, or you can enjoy it afterwards – it's also included in your ticket.
Join us on this intimate discovery of two countries in one day!
|Adult (16-99)
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text: Thank you so much to Charlotte's Corner for this nomination – it's an opportunity that Freedom is so happy to accept! But it has never been about what we do for ourselves, but rather what we can do for others. So Freedom would like to nominate the following bloggers for this amazing Sisterhood award. Continue reading


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Biltmore Commons Apartments in Portsmouth, VA
Located at 2341 Elliott Avenue, Portsmouth, VA 23704
2341 Elliott Avenue, Portsmouth, VA 23704
Large Closets, On-Site Laundry Facilities, Mini Blinds, Some Newly Renovated Apartments Available, Breakfast Bar, Separate Dining Area
We cannot find any listings from Biltmore Commons Apartments on Marketplace. Check out these other listings near Portsmouth.
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tag:blogger.com,1999:blog-5608551339547821473.post1165124352124964428..comments2009-08-30T22:06:33.377-07:00Comments on Mortgage Refinancing: Mortgage Refinancing Secrets Revealedclementhttp://email@example.com:blogger.com,1999:blog-5608551339547821473.post-4358929914702715142009-08-30T22:06:33.377-07:002009-08-30T22:06:33.377-07:00Hello Webmaster I am Susan Thomas. I am a webma...Hello Webmaster

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MLK's Mother Was Assassinated Too — Alberta Williams King's Martyrdom Has Been Forgotten
White supremacist US historiography has erased the brave struggles of countless people of color. It has whitewashed (pun intended) the history of valiant resistance against unimaginable oppression.
Patriarchal historiography has done the same to women. For women of color, this erasure has been doubly destructive.
Because of this intersectional double oppression (two parts of what black feminist marxist Claudia Jones called "triple oppression" — the last form being class struggle), we must be extra diligent in ensuring that our historical narratives do not ignore the women of color who sacrificed their lives for a more just and free world.
In a prime example of such whitewashing at work, one should take a look at the mother of one of the most deradicalized figures of all, Martin Luther King, Jr.
Alberta Williams King, who raised MLK, was also assassinated for her political activism, for fighting against obscene injustice. Few, however, remember her crucial legacy.
In "MLK's Mother Was Assassinated, Too: The Forgotten Women of Black History Month," journalist and playwright Aurin Squire reflects on this unfortunate phenomenon:
On June 30th, 1974, Alberta Williams King was gunned down while she played the organ for the "Lord's Prayer" at Ebenezer Baptist Church. As a Christian civil rights activist, she was assassinated… just like her son, Martin Luther King, Jr. But most people remember only one. Until a month ago, I was one of those people.
When a friend told me about Alberta Williams King, my first reaction was "who?" This question was followed by a wave of shame. It was the same feeling I had a few years ago when I first heard about Fannie Lou Hamer. Then later came Ida B. Wells and other leaders who seemed to appear in the discussion of American history to my confused, uninformed silence. I started to suspect that I had half an education and that I had been leaving out the role of women and feminism in Black History.
I thought I was fairly well-versed in African-American history. My parents filled our shelves with the core curriculum: Up From Slavery, Letters from a Birmingham Jail, Native Son, Black Boy, Go Tell it On the Mountain, Soul on Ice, The Miseducation of the Negro, Before Columbus, and many more pieces of literature and non-fiction. I immersed myself in books, hagiography, essays, videos, encyclopedias. My extracurricular studies came from an authentic curiosity (instead of dutiful obligation) to know more about my family. Black females held the role of poetry and song: Phillis Wheatley, Maya Angelou, Gwendolyn Brooks, Nikki Giovanni, Sapphire. But as far as activism and leadership, the ranks were all-male.
When my teachers lectured about Mideast history, they had to mentally sever Egypt, Libya, and most of the region from the African continent just to keep the Eurocentric/Mediterranean conceit in tact. But none of this surprised me. Most of my white peers reached a consensus that African-Americans didn't really have a history before slavery (or the arrival of Europeans) and not much to talk about after the civil rights movement of the 1960s. The only subcategory with an even smaller claim to historical significance were Black women. And while a fierce argument would rage in defense of the need for black history, most were willing to concede the importance of that history's feminism.
Now this isn't meant to be a diatribe against my teachers, family, or community. I'm grateful for their lessons on African-American men who made black history. But after hearing about Alberta Williams King last month having no idea of who she was, I began to wonder how many transformational stories I had been missing. With the exception of Sojourner Truth and Harriet Tubman, I had neglected one half of my story.
Even now, as the nation's attention focuses on the new generation of activists fighting against police brutality and hate crimes, it's women who are often left out. The silence has subtle but lasting consequences. Historical omission points toward a culture's subconscious beliefs that some people matter less than others. When female stories are muted, we are teaching our kids that their dignity is second class and the historical accounts of their lives are less relevant. This lowered value carries over when women face sexual objectification and systemic brutalization from inside and outside the community. When we can't see ourselves in our history, we begin to think that we are disconnected and suffering alone. Historical ignorance always precedes cultural imbalances and individual despair. Too many lives are still lived in the blank space, too many march for racial equality while subjugating their gender and even sexual orientation.
The wave of inter- and intra-community violence against women and African American LGBT citizens is not an accident. It may seem like nit-picking to talk about the lack of non-heteronormative stories during Black History Month. But historical exclusivity often has a way of turning into present and institutionalized tragedy. Whose story gets told matters.


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Ministry of Social Security and Labour (Lithuania)
|Lietuvos Respublikos socialinės apsaugos ir darbo ministerija
|Ministry overview
|Vilnius, Lithuania
|Minister responsible
The Ministry of Social Security and Labour of the Republic of Lithuania (Lithuanian: Lietuvos Respublikos socialinės apsaugos ir darbo ministerija) is a government department of the Republic of Lithuania. Its operations are authorized by the Constitution of the Republic of Lithuania, decrees issued by the President and Prime Minister, and laws passed by the Seimas (Parliament). Its mission is to prosecute state social security and labour branch ruling functions and realize state policy in these branches.[1] The current head of the Ministry is Algimanta Pabedinskienė.
See also[edit]
- Other ministries of Social Security
- Other ministries of Labour
|This article about politics in Lithuania is a stub. You can help Wikipedia by expanding it.


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Sometimes I think about the new types of conversations I have every day now that I don't work in an office.
Here are some normal conversations in my life now
Me- Okay, Kk you still need pants
K-I don't know how to wear pants
Me-Interesting, you have tried on at least three different pairs today
K- It's too hard
I get it, life is better without pants
Then there's this-
Me- Go climb in the car while I grab the snack bag
*I go outside and the kids are standing on the porch
Me-What are you doing?
E- It was too far
Me-It's like ten steps
E-Yeah we were too scared
Me-Of what
E-There was a fly
Me- what?
Got it..glad we aren't overeacting
This one occurred in the car. K was singing loudly while E was planning every detail of her birthday party.
As Eden and I were talking- K was shouting "Maaaaade the children laugh and play" on repeat in the background. Just that one line from Mary had a little lamb…over and over again.
E- Mommy I am going to wear the splatter paint dress on my birthday.
Me- that sounds lovely
K-maaaaaaaaade the children laugh and play
E- Do I get to choose what everyone wears to my party?
K-maaaaaaaaade the children laugh and play
Me- What? No, you get to choose for your body, and they get to choose for their bodies
E- Oh…What if I don't like what they are wearing?
K- Teeeeeeeeacher looooocked the lamb at school
Me- sometimes that happens, but, everyone gets to make their own choices for their bodies, even though it's your birthday
E- hmmm…ok
K- Teeeeeeeeacher looooocked the lamb at school
*k sings the new line for several minutes before I realize what she's saying
Me- Kk are you saying the teacher locked the lamb at school??
K- Yes
Me- What? I'm quite certain that's not the song
K- *pauses for a moment "Teeeeeeeeacher looooocked the lamb at school"
Just another day.


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Villa Tanju Bali
Approximately 50 minutes from Bali`s International airport and 25 minutes from the popular district of Seminyak, Villa Tanju Bali consists of four air-conditioned bedrooms with private bathroom en-suites. Each bedroom is made with the finest materials and tastefully decorated. There are safety deposit boxes in all bedrooms.
The fully equipped kitchen is large, and of western standards, featuring a cook-top, oven, double-door refrigerator, coffee machine and appliances. The villa menu allows it guests to choose from a number of Indonesian and Western dishes. A stunning open-air dining pavilion is surrounded by stunning ocean views which seats 10 persons comfortably with terazzo floors and alang-alang (grass thatched) roofing. There are 11 staff in total including night security.
Upstairs is a study which can be converted into a fifth bedroom if required. The study area features a large sofa, computer with broadband Internet connection, plasma television, satellite connection, DVD player, library and games. The large terrace outside overlooks the ocean, manicured lawns and gardens.
Perfectly positioned by the swimming pool is a glass door living area, with large comfortable sofa's and cushions. The infinity swimming pool (15m x 4.5m) sits on the edge of the property complimented by a relaxing Buddha statue, day loungers and umbrellas. There is an outdoor shower and at night the gardens glow with torches.
|Four Bedrooms
Low Season
January 11, 2017 - January 26, 2017
February 02, 2017 - April 13, 2017
April 22, 2017 - June 30, 2017
September 01, 2017 - December 19, 2017
High Season
January 27, 2017 - February 01, 2017
April 14, 2017 - April 21, 2017
July 01, 2017 - August 31, 2017
Peak Season
December 20, 2017 - January 10, 2018
Term and Condition
- Rates are quoted in US dollars subject to 15.5% Government Tax and service charge
- Rates are categorized in low and high seasons only
- Rates include full staff and WI-FI
- Minimum stay of 5-10 nights is required in Low season, 10 nights in high season
- Check in time is 2:00pm, late check out after 6pm may incur a half-day rental fee
- A 50% deposit is required to secure your reservation
- Balance of payment due 45 days prior to your arrival, or immediately if less than 45 days
- Cancellation fees will apply if booking is cancelled less than 61 days
- Final payments are non-refundable
- Villa Tanju Bali will not be liable to their guests for any personal injury, sickness, loss or damage to their personal belongings during their stay and any inconvenience arising from exceptional weather conditions or unique Balinese festivals. Every effort is made to ensure that the villa is presented to meet its required standard for all bookings
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Bataille des Eperons d'Or (Battle of the Golden Spurs)
James Ensor (1860-1949),ĀBataille des Eperons d'Or (Battle of the Golden Spurs), etching, 1895, signed, dated and titled in pencil. On laid paper with a Honig watermark, with margins. References: Delteil 95, Elesh 98, only state. 7 x 9 5/8, the sheet 9 1/4 x 12 3/4 inches.
Provenance: Mira Jacob Wolfovska (1912-2004), Paris, with her blindstamp (not in Lugt).
An extremely fine impression, black ink on a cream paper, with plate tone slightly accentuated toward the bottom of the sheet and away from the sky.
The story of the Battle of the Golden Spurs relates how, in 1302, the over-confident French nobility was defeated by Flemish peasant soldiers. In 1890 Ensor made a satirical drawing of the battle titled Le Souper des Veterans (The Veteran's Supper). In the etching, although the French are making a massive frontal attack, they appear in large part to be enduring a counter-attack in the rear.