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0 | 0 | latent semantic analysis | mathematical procedure for automatically extracting and representing the meanings of propositions expressed in a text. | a program that computes meaning by assessing words in similar contexts; it can tell if two words have similar meanings even if they rarely occur together |
3 | 1 | data integration | the integrations of data from multiple sources, which provides a unified view of all data. | seamlessly combining data from multiple sources which provides a unified/consistent view of all data (ex. microsoft outlook calendar) |
3 | 1 | data integration | comprises 3 major processes that permit data to be accessed to an array of etl and analysis tools and the dw | comprises three major process that when correctly implemented permit data to be assess and made accessible to an array of etl: data access, data federation, and change capture. |
2 | 1 | data integration | being able to process and combine data that has no similar structure or source | combination of tehcnical and business processes used to combine data from disparate sources into valuable information |
0 | 0 | data integration | seamlessly combining data from multiple sources which provides a unified/consistent view of all data (ex. microsoft outlook calendar) | the integration of data from multiple sources |
1 | 0 | data integration | seamlessly combining data from multiple sources which provides a unified/consistent view of all data (ex. microsoft outlook calendar) | creates a unified view of business data other possibilities: -application integration -business process -user interaction integration. |
2 | 1 | data integration | integration that comprises three major processes: data access, data federation, and change capture | involves combining data residing in different sources and providing users with a unified view of this data |
0 | 0 | data integration | combining data from multiple files may be difficult | ability to access all pertinent data about an entity wherever the data may exist |
2 | 1 | data integration | achieved by combining master files into larger pools of data accessible by many programs | files are logically combined and made accessible to various systems - cross functional analysis - master files combined into large pool of data |
0 | 0 | data integration | permit data to be accessed and made accessible to an array of etl, analysis tools, and data warehouse environment | comprises three major process that when correctly implemented permit data to be assess and made accessible to an array of etl: data access, data federation, and change capture. |
2 | 1 | data integration | (database benefit) master files are combined into large &"pools&" of data that many application programs access | master files are combines into large &"pools&" of data that many application programs access. an example is an employee database that consolidates payroll, personnel, and job skills master files |
3 | 1 | data integration | combination of tehcnical and business processes used to combine data from disparate sources into valuable information | - the combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information - integrate data from multiple sources |
0 | 0 | data integration | integration that comprises three major processes: data access, data federation, and change capture | process based on data flows with different data sources and applications |
2 | 1 | data integration | involves combining data residing in different sources and providing users with a unified view of this data | taking data from multiple sources and combining it. you will need high quality data in the right format in order to be able to analyze data. |
1 | 0 | data integration | the integrations of data from multiple sources, which provides a unified view of all data. | creates a unified view of business data other possibilities: -application integration -business process -user interaction integration. |
1 | 0 | data integration | involves combining data residing in different sources and providing users with a unified view of this data | process based on data flows with different data sources and applications |
1 | 0 | data integration | the integration of data from multiple sources | creates a unified view of business data other possibilities: -application integration -business process -user interaction integration. |
3 | 1 | data integration | combination of technical and busines processes used to combine data from disparate sources into meaningful and valuable information. three major processes: data access, data federation, and change capture | - the combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information - integrate data from multiple sources |
2 | 1 | data integration | achieved by combining master files into larger pools of data accessible by many programs | files are logically combined and made accessible to various systems |
1 | 0 | data integration | integration that comprises three major processes: data access, data federation, and change capture | taking data from multiple sources and combining it. you will need high quality data in the right format in order to be able to analyze data. |
0 | 0 | information gain | a measure on how well an attribute will split up the remaining data into disjunct groups | difference between entropy between before and after split infogain(f) = entropy(s1)-entropy(s2) =post-pre split |
3 | 1 | information gain | the change in entropy that would result from a split on each possible feature. | difference between entropy between before and after split infogain(f) = entropy(s1)-entropy(s2) =post-pre split |
2 | 1 | information gain | approach to reduce splits in decision tree and get highest amount of certainty | a way to decide the best attribute for a decision tree split |
2 | 1 | information gain | a measure on how well an attribute will split up the remaining data into disjunct groups | to determine the optimal feature to split upon, the decision tree algorithm calculates the change in entropy that would result from a split on each possible feature. |
0 | 0 | information gain | measures how much &"information&" a feature gives us about the class. | also referred to as mutual information, helps us to measure the dependence between two variables. |
2 | 1 | information gain | - measure of impurity - the reduction in entropy resulting from a chosen split - so you want to maximize info gain for each split | difference between entropy between before and after split infogain(f) = entropy(s1)-entropy(s2) =post-pre split |
1 | 0 | information gain | measures the reduction of the entropy caused by the partitioning the examples according to the chosen attribute. | the expected reduction in entropy |
0 | 0 | information gain | the measure used to select the best attribute at each step in growing decision trees | information you have now that you didn't have before |
2 | 1 | information gain | difference between entropy between before and after split infogain(f) = entropy(s1)-entropy(s2) =post-pre split | to determine the optimal feature to split upon, the decision tree algorithm calculates the change in entropy that would result from a split on each possible feature. |
2 | 1 | information gain | the measure used to select the best attribute at each step in growing decision trees | approach to reduce splits in decision tree and get highest amount of certainty |
1 | 0 | information gain | a measure on how well an attribute will split up the remaining data into disjunct groups | - measure of impurity - the reduction in entropy resulting from a chosen split - so you want to maximize info gain for each split |
0 | 0 | information gain | information you have now that you didn't have before | a way to decide the best attribute for a decision tree split |
1 | 0 | information gain | uses entropy to measure the extent of uncertainty or randomness of a particular attribute/value split used in id3, c4.5, c5 | a measure of the predictive power of one or more attributes |
0 | 0 | information gain | uses entropy to measure the extent of purification obtained from a particular attribute/value split | reduction of entropy (relative to the target attribute). higher ig indicates a more informative split |
2 | 1 | information gain | a mathematical way to capture the amount of information one gains by picking a particular attribute | measures how well a given attribute separates the training examples according to their target classification |
1 | 0 | information gain | the difference between the starting entropy and the expected entropy | the difference between the initial entropy of a node and the entropy of the node after its been split. |
3 | 1 | information gain | the measure used to select the best attribute at each step in growing decision trees | a way to decide the best attribute for a decision tree split |
0 | 0 | common features | deciding if something is part of a concept based on whether certain very common elements are present | are spell checkers alignments , fonts and font size, character effects and edit options. |
2 | 1 | control system | an arrangement of physical components connected or related in such a manner as to command, direct or regulate itself or another system | computer/ microprocessor will monitor physical properties and based on comparison with pre stored values,take action that can alter physical properties. eg. air conditioning |
1 | 0 | control system | computer/ microprocessor will monitor physical properties and based on comparison with pre stored values,take action that can alter physical properties. eg. air conditioning | an interconnection of components forming a system configuration that will provide a desired response |
0 | 0 | control system | interpret operator settings and produce and regulates the desired output | an instrumentation system that senses and controls its own operation on a close, continuous basis in what is called proportional (or modulating) control. |
1 | 0 | control system | an interconnection of components forming a system configuration that will provide a desired response | a device (or set of devices) that manages, commands, directs or regulates the behaviour of other devices or systems. (ipo) |
0 | 0 | control system | an arrangement of physical components connected or related in such a manner as to command, direct or regulate itself or another system | an interconnection of components forming a system configuration that will provide a desired response |
1 | 0 | control system | a collection of controllers, sensors and actuators that together respond to changes in their environment or settings | a system of sensors and actuators operating under the direction of a controller |
0 | 0 | image segmentation | - partitions an image into meaningful regions called segments - complementary approach to edge detection: id boundaries of objects after segmentation has occurred | - identifying meaningful regions - group pixels according to local image properties - |
1 | 0 | image segmentation | - identifying meaningful regions - group pixels according to local image properties - | dividing images into semantically meaningful regions |
2 | 1 | image segmentation | - partitions an image into meaningful regions called segments - complementary approach to edge detection: id boundaries of objects after segmentation has occurred | dividing images into semantically meaningful regions |
2 | 1 | image segmentation | divide image in regions with pixels of similar qualities | selection of specific regions based on: image intensity (color) location shape texture |
1 | 0 | deadlock detection | the dbms periodically tests the database for deadlocks if a deadlock is found, one of the transactions (&"victim&") is aborted, and the other transaction continues. | test for deadlocks periodically and abort one of the transactions |
0 | 0 | deadlock detection | the dbms periodically tests the database for deadlocks | test for deadlocks periodically and abort one of the transactions |
1 | 0 | deadlock detection | dbms checks them periodically, if it is found, the victim transaction is aborted | the dbms periodically tests the database for deadlocks if a deadlock is found, one of the transactions (&"victim&") is aborted, and the other transaction continues. |
1 | 0 | deadlock detection | handled by the construction of wait-for graph which shows transaction dependencies; deadlock exists if the wfg contains a cycle | a system is in a deadlock state <=> the wait-for graph has a cycle. looks for cycles periodically to detect deadlock. |
2 | 1 | deadlock detection | dbms checks them periodically, if it is found, the victim transaction is aborted | dbms tests db for a deadlock, if a deadlock is found a victim transaction is rolled back, the other transaction continues |
3 | 1 | deadlock detection | dbms tests db for a deadlock, if a deadlock is found a victim transaction is rolled back, the other transaction continues | the dbms periodically tests the database for deadlocks if a deadlock is found, one of the transactions (&"victim&") is aborted, and the other transaction continues. |
2 | 1 | deadlock detection | dbms checks them periodically, if it is found, the victim transaction is aborted | the dbms periodically tests the database for deadlocks |
1 | 0 | deadlock detection | dbms tests db for a deadlock, if a deadlock is found a victim transaction is rolled back, the other transaction continues | test for deadlocks periodically and abort one of the transactions |
2 | 1 | deadlock detection | dbms tests db for a deadlock, if a deadlock is found a victim transaction is rolled back, the other transaction continues | the dbms periodically tests the database for deadlocks |
0 | 0 | reinforcement learning | a computer learns from interacting with itself (or data generated by the same algorithm) | a machine learning algorithm where the algorithm interacts with an environment, creating a feedback loop between the learning system and its experiences |
0 | 0 | reinforcement learning | not a mixture of supervised/unsupervised learning. different set-up. agent within environment that: peas (performance, environment, actuators, sensors) aim: find optimal actions | making an agent learn to maximize output |
0 | 0 | reinforcement learning | not a mixture of supervised/unsupervised learning. different set-up. agent within environment that: peas (performance, environment, actuators, sensors) aim: find optimal actions | an agent is given information about an environment and asked to make moves that self-driving cars, education, optimal resource management, robotics |
2 | 1 | reinforcement learning | works through trial and error which actions yield the greatest rewards | learning through practice or trial and error - learning system is set loose to find a solution for itself and if it succeeds it is given a reward |
2 | 1 | reinforcement learning | learning by 'trying' a response and being 'punished' or 'rewarded' depending on whether the response was the desired one. | works through trial and error which actions yield the greatest rewards |
0 | 0 | reinforcement learning | an agent is given information about an environment and asked to make moves that self-driving cars, education, optimal resource management, robotics | making an agent learn to maximize output |
1 | 0 | reinforcement learning | not a mixture of supervised/unsupervised learning. different set-up. agent within environment that: peas (performance, environment, actuators, sensors) aim: find optimal actions | a type of machine learning, and thereby also a branch of artificial intelligence. allows machines to calibrate their models by receiving rewards or &"points&" for good performance. |
0 | 0 | reinforcement learning | learning from rewards, by trial and error, during normal interaction with the world | machine is trained to take specific decisions that maximize efficiency |
3 | 1 | reinforcement learning | learning from rewards, by trial and error, during normal interaction with the world | software agents interact with an environment: - learn how to optimize their behavior - given a system of rewards and punishments - draw inspiration from behavioral psychology |
1 | 0 | reinforcement learning | a type of machine learning, and thereby also a branch of artificial intelligence. allows machines to calibrate their models by receiving rewards or &"points&" for good performance. | making an agent learn to maximize output |
0 | 0 | reinforcement learning | machine is trained to take specific decisions that maximize efficiency | software agents interact with an environment: - learn how to optimize their behavior - given a system of rewards and punishments - draw inspiration from behavioral psychology |
0 | 0 | reinforcement learning | a type of machine learning, and thereby also a branch of artificial intelligence. allows machines to calibrate their models by receiving rewards or &"points&" for good performance. | an agent is given information about an environment and asked to make moves that self-driving cars, education, optimal resource management, robotics |
3 | 1 | reinforcement learning | learning by 'trying' a response and being 'punished' or 'rewarded' depending on whether the response was the desired one. | learning through practice or trial and error - learning system is set loose to find a solution for itself and if it succeeds it is given a reward |
1 | 0 | data warehousing | is the practice of taking and storing data in a data warehouse (large business system) that supports business intelligence (bi) | used for business intelligence - big database used to pull in very large and complex data sets |
1 | 0 | data warehousing | is the practice of taking and storing data in a data warehouse (large business system) that supports business intelligence (bi) | &"pre-summarized; not pulling data&" -a logical collection of information gathered from many different operational databases |
0 | 0 | data warehousing | &"pre-summarized; not pulling data&" -a logical collection of information gathered from many different operational databases | a system used for reporting and data analysis, and is considered a core component of business intelligence; data is stored in one place. |
0 | 0 | data warehousing | is the practice of taking and storing data in a data warehouse (large business system) that supports business intelligence (bi) | massive data stores of time series data for decision support |
0 | 0 | data warehousing | massive data stores of time series data for decision support | used for business intelligence - big database used to pull in very large and complex data sets |
0 | 0 | data warehousing | used for business intelligence - big database used to pull in very large and complex data sets | a system used for reporting and data analysis, and is considered a core component of business intelligence; data is stored in one place. |
2 | 1 | data warehousing | enterprise application -logically centralized large db--entities + attributes -physically centralized/distributed--servers + data -powerful enterprise-wide querying applications | -is a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management's decision making process. -data grows, doesn't really change -logically centralized large database -powerful enterprise-wide querying applications |
1 | 0 | data warehousing | is the practice of taking and storing data in a data warehouse (large business system) that supports business intelligence (bi) | a logical collection of information - gathered from many different operational databases - that supports business analysis activities and decision-making tasks |
2 | 1 | data warehousing | -collections of data from different information systems -transforming the data into a consistent format is important | the process of combining data from multiple databases or data sources in a central location called a warehouse. |
0 | 0 | data warehousing | creates a well-planned information management solution to enable analytical and informational processing despite platform, application, organizational, and other barriers. | the collection and storage of large amounts of data from a range of sources, in order to support the future decisions of management. this data is often historical. |
3 | 1 | data warehousing | integrates data from multiple different sources and stores it in a uniform manner; a central data repository | the process of combining data from multiple databases or data sources in a central location called a warehouse. |
1 | 0 | data warehousing | use of info systems facilities to focus on the collection, org, integration, and long-term storage of entity-wide data | creates a well-planned information management solution to enable analytical and informational processing despite platform, application, organizational, and other barriers. |
0 | 0 | data warehousing | massive data stores of time series data for decision support | -data warehouse: a logical collection of information gathered from many different operational databases (extract, transform, load) used to create business intelligence that supports business analysis activities and decision-making tasks. |
1 | 0 | data warehousing | the process of combining data from multiple databases or data sources in a central location called a warehouse. | starts with data stored in different systems and often with inconsistencies (in terminology, formats, and so on), and converts it into data stored in a single logical repository |
2 | 1 | data warehousing | massive data stores of time series data for decision support | &"pre-summarized; not pulling data&" -a logical collection of information gathered from many different operational databases |
2 | 1 | data warehousing | -collections of data from different information systems -transforming the data into a consistent format is important | the process of collecting the data from different data sources within an organization and storing it in a single database that can be used for decision making |
0 | 0 | data warehousing | use of info systems facilities to focus on the collection, org, integration, and long-term storage of entity-wide data | the collection and storage of large amounts of data from a range of sources, in order to support the future decisions of management. this data is often historical. |
3 | 1 | data warehousing | -data warehouse: a logical collection of information gathered from many different operational databases (extract, transform, load) used to create business intelligence that supports business analysis activities and decision-making tasks. | a logical collection of information - gathered from many different operational databases - that supports business analysis activities and decision-making tasks |
0 | 0 | data warehousing | -collections of data from different information systems -transforming the data into a consistent format is important | the process allowing important day-to-day operational data to be stored and organized for simplified access. - amazon's redshift |
0 | 0 | data warehousing | used for business intelligence - big database used to pull in very large and complex data sets | a logical collection of information - gathered from many different operational databases - that supports business analysis activities and decision-making tasks |
0 | 0 | data warehousing | massive data stores of time series data for decision support | a logical collection of information - gathered from many different operational databases - that supports business analysis activities and decision-making tasks |
0 | 0 | data warehousing | the process allowing important day-to-day operational data to be stored and organized for simplified access. - amazon's redshift | starts with data stored in different systems and often with inconsistencies (in terminology, formats, and so on), and converts it into data stored in a single logical repository |
1 | 0 | data warehousing | the process allowing important day-to-day operational data to be stored and organized for simplified access. - amazon's redshift | the process of combining data from multiple databases or data sources in a central location called a warehouse. |
3 | 1 | data warehousing | combining multiple data sources into a large database for retrieval and analysis | combines data from multiple sources into a large database wit the purpose of extensive retrieval and trend analysis |
2 | 1 | data warehousing | the use of a database or a collection of databases developed for an organization or an enterprise for analysis and support of management decisions | the collection and storage of large amounts of data from a range of sources, in order to support the future decisions of management. this data is often historical. |
1 | 0 | data warehousing | use of info systems facilities to focus on the collection, org, integration, and long-term storage of entity-wide data | the use of a database or a collection of databases developed for an organization or an enterprise for analysis and support of management decisions |
0 | 0 | data warehousing | the use of a database or a collection of databases developed for an organization or an enterprise for analysis and support of management decisions | creates a well-planned information management solution to enable analytical and informational processing despite platform, application, organizational, and other barriers. |
2 | 1 | data warehousing | the process allowing important day-to-day operational data to be stored and organized for simplified access. | the collection and storage of large amounts of data from a range of sources, in order to support the future decisions of management. this data is often historical. |
0 | 0 | data warehousing | use of info systems facilities to focus on the collection, org, integration, and long-term storage of entity-wide data | the process allowing important day-to-day operational data to be stored and organized for simplified access. |
0 | 0 | data warehousing | the use of a database or a collection of databases developed for an organization or an enterprise for analysis and support of management decisions | the process allowing important day-to-day operational data to be stored and organized for simplified access. |
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