text:
|Publication number
|Publication type
|Application number
|Publication date
|Nov 8, 1994
|Filing date
|May 28, 1991
|Priority date
|May 28, 1991
|Fee status
|Publication number
|Salvatore J. Stolfo, Ouri Wolfson, Hasanat Dewan
|Original Assignee
|The Trustees Of Columbia University In The City Of New York
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (5), Non-Patent Citations (10), Referenced by (65), Classifications (8), Legal Events (8)
|External Links: USPTO, USPTO Assignment, Espacenet
The present invention relates to computerized inference systems as applied to database systems.
Inference or rule-based systems include a knowledge base (or database) which consists of a set of facts, a set of rules applicable to the facts, and an inference procedure for producing new facts by applying rules to existing facts. Thus, the database changes as a result of inference activity carried out by a rule interpreter that evaluates rules. Typically, moreover, such systems assume a static environment in the sense that changes to the database take place only as a result of rule-program evaluation, so that any change in the initial database requires restarting the system. These systems are not capable of efficiently incorporating modifications to the database of facts resulting from sources other than the actions of the rules themselves.
In the interest of enhanced efficiency of a computerized inference system, the invention provides for updating a current database without restarting the system. Rather, changes are made directly in a current database to reflect a change in the original database from which the current database was produced.
FIG. 1 is a schematic representation of a communication network whose operation may involve use of a preferred method and system in accordance with the invention;
FIG. 2 is a block diagram of a method in accordance with a preferred embodiment of the invention;
FIG. 3 is a block diagram of updating in accordance with a preferred embodiment of the invention;
FIG. 4 is a schematic representation of a status record as may be used in a preferred embodiment of the invention; and
FIG. 5 is a schematic diagram of a system in accordance with a preferred embodiment of the invention.
In real time applications, e.g., communication network management, the database may change independently of the inference process, since usually one cannot afford the luxury of collecting all the relevant facts before starting the inference procedure. Relevant information may arrive after inferencing has already begun. Consider, for example, an expert analyzer for automatically detecting a fault in a communication network that is in operation, and which is continuously producing information relevant to the detection process. In this environment, messages may arrive from time to time that nullify previous assumptions. For example, suppose the expert analyzer has assumed that some link LINK A-- B is up when the diagnosis of some problem was started, and at some later point, say at iteration n of the standard match-select-act cycle, a message arrives indicating that LINK A-- B is down. The naive approach for incorporating this late arriving information would be to restart the expert analysis from the beginning. However, considering that network management data is being produced continuously, this strategy may result in an infinite regression and the inference process may never terminate.
In prior art truth maintenance systems designed for incremental evaluation, such as Jon Doyle's "A Truth Maintenance System; Readings in Artificial Intelligence", Morgan Kaufmann 1981, pp. 496-516; A. Barr and E. Feigenbaum, eds., "The Handbook of Artificial Intelligence", Vol 2; Raymond Reiter and Johan de Kleer, "Foundations of Assumption-Based Truth Maintenance Systems", Preliminary Report, Proceedings of AAAI-87, pp. 183-188; and K. R. Apt and J. Pugin: "Maintenance of Stratified Databases Viewed as a Belief Revision System", Proc. 6th ACM Symp. on PODS pp. 136-145 (1987), the systems employed a truth maintenance system which built a dependency graph having nodes that correspond to base and derived facts. A problem with these systems is that in data intensive applications, space requirements for maintaining such dependency graphs become prohibitively expensive. This problem is further exaggerated by the fact that the dependency graph in such systems requires the representation of negative facts in the database in order to support the graph.
Closely tied to the question of incremental updates to a database is the semantics of the underlying rule language. For DATALOG programs, where negation is not allowed in either the head or body of a rule, incremental processing is simple. Consider for example the semi-naive evaluation of the linear DATALOG program that computes the transitive closure of a graph as described in J. D. Ullman's "Principles of Database and Knowledge Base Systems", Computer Science Press, 1989. Incorporation of a newly arrived arc of the graph is easy. The arc is simply added to the differential, and the evaluation proceeds as usual, until a fixpoint is reached. This can be done regardless of whether the new arc arrived before or after the transitive closure evaluation has completed. Since DATALOG is strictly monotonic, incremental update involves only recomputing the consequences of the newly arrived arc.
On the other hand, retraction of an arc from the graph, even in the simple transitive closure example, is a more subtle non-monotonic process. Consider languages that allow negation in the body and head of rules (negation in the head means deletion). Suppose that the fact f is retracted from a database after some inferences that depend on f have occurred. The high level view of subsequent processing that must take place is the following: the facts that were derived from f (and recursively, their derivatives) must be backed out or undone, and then the facts that can be derived from - f (and their derivatives) must be redone. For DATALOG programs (that do not have negative atoms in the rules), no actions are derived from negative facts. Therefore, for a retracted fact we only need an undo phase, and for an asserted fact we only need a redo phase. However, for more powerful rule languages, we need both an undo and a redo phase to incrementally update the database.
These two phases are not always straightforward. Rule languages such as OPS5 and DATALOG * usually operate in match-select-act cycles, and at each cycle (or iteration), some conflict resolution strategy is applied (note: in DATALOG, the symbol indicates that negations are allowed in bodies and the symbol * indicates that negations are allowed in the heads. A more detailed explanation of the DATALOG * language is described infra.) Now, consider redoing the consequences of f for the above example, This involves matching all the rules (or some sub-set of them in a language such as STRATIFIED DATALOG) and considering the instantiations that have f in the body. Suppose that the head of such an instantiation is add-- e. This does not mean that we can simply add e to the database during the redo phase for the following reason. The incremental procedure must produce the same result as if f were in the initial database. If f were in the initial database, it is possible that the add-- e operation would have been eliminated by a conflict resolution strategy within the language procedure. In the language DATALOG, add.sub. -- e would have been cancelled out by a delete-- e operation in the same cycle; moreover, the delete-- e operation may not be a consequence of f. Similarly, in OPS5 add-- e would not have been executed had there been a more recent instantiation in the cycle. Therefore, the incremental procedure must have enough information to determine for each redo instantiation whether or not it would have been eliminated by the conflict resolution strategy. Furthermore, this information obviously depends on the conflict resolution strategy, which in turn depends on the rule language.
Now consider the data reduction paradigm for distributed and parallel rule processing, as described in S. J. Stolfo, D. P. Miranker and R. Mills, "A Simple Processing Scheme to Extract and Load Balance Implicit Parallelism in the Concurrent Match of Production Rules", Proceedings of the A.F.I.P.S. Symp. on Fifth Generation Computing, AFIPS, 1985. It stipulates that each processor evaluates the original rule program, but with less data. The operations of each processor that result from the evaluation (e.g. add-a-fact or delete-a-fact) are transmitted to other processors that may use these to derive other operations. Now, if the processors are synchronized at each cycle, meaning that they all complete their evaluation and transmission in a certain cycle before any processor proceeds to the next one, then incremental evaluation is not necessary. However, this implies that the evaluation proceeds at the speed of the slowest processor at each iteration, which is not acceptable in a distributed setting. Suppose therefore that the evaluation proceeds asynchronously. Then it is possible that a processor p1 that is evaluating at cycle 5 receives an add-- e from a processor p2 that is evaluating at cycle 3. This means that p1 has to incorporate e into its local database of cycle 3. Then the problem it faces is exactly the problem of incremental rule processing: it has to undo the consequences of -e at cycle 3, and redo the consequences of e at cycle 3, but without backing its whole rule evaluation to cycle 3. In other words, individual processors need to have the ability to incorporate changes from other processors in arbitrary previous iterations. In contrast, for real time rule processing the update is always to the input database, namely to cycle 0.
The present invention provides a method and apparatus for performing only those actions in an inference mechanism that are necessary to incrementally bring the database to a consistent state.
The present invention also provides a method and apparatus where incremental updating may be performed in a recursive manner in which it can operate for an arbitrary cycle update such that it may be applied to asynchronous parallel-and-distributed rule processing by data reduction.
The technique herein described for incrementally updating a database can be summarized as follows. In the context of the previous example, wherein it was initially assumed that link LINK A-- B was up and, subsequent to commencing inferential processing, a message arrived indicating link LINK A-- B was down, a system employing the present technique would only undo the consequences of the initial assumption that LINK A-- B was up, and redo the consequences of the newly received fact that LINK A-- B is down. In other words, only the consequences associated with the newly arrived information would be revised.
This simplified example is functionally illustrated in FIGS. 1-3. In FIG. 1, LINK A-- B represents the connection between two communication nodes, node A and node B in a communication system. In this example, the goal is to establish a communication line from our point of origin, namely node W, to our destination, node E. The nodes are connected by a series of links which can either be up, useable for connection from an initial node to a destination node, or they can be down, wherein no connection can be made between the nodes. A connection can only be made by connecting enough continuous links to directly connect the point of origin to the point of destination under constraints developed in the inference program. In real life applications, constraints may include the selection of links which have less traffic; however, in this example we will simply choose the selection of the minimum number of links to connect any two points. If we commence with the assumption that LINK A-- B is up and the system's inferential process causes a connection through LINK A-- B, the consequences of having LINK A-- B go down would be catastrophic to the system's goal. Simply stated, the system will have established a faulty connection. Thus, if a processing system received a message adding the new fact that link LINK A-- B was down to the initial database, as illustrated in FIG. 2, the system would need to address the new fact. In the system of the present invention, rather than restart the inference process from the beginning, the system would only undo the consequences of LINK A-- B being up, then these LINKS would be disengaged, and the system would redo the consequences of LINK A-- B being down, as asserted in FIG. 3. In other words, the system would find another route for connecting nodes W and E under the prescribed constraints of the program without starting over. For instance, the system would disengage LINK A-- B, LINK T-- A and LINK B-- C; engage LINK T-- X, LINK X-- Z and LINK Z-- C; and maintain the other previous links, namely LINK W-- V, LINK V-- U, LINK U-- R, LINK R-- T, LINK C-- D, and LINK D-- E.
In another example using FIG. 1, if the inference system had previously established that a connection could be made from nodes W to E by passing through nodes T and C, and that nodes T and C could be connected by either routes T-- A-- B-- C or T-- X-- Z-- C, then a subsequent update message indicating LINK A-- B to be down would require the system to backtrack only up to the point where it was inferenced that nodes T and C could be connected through one of two paths. Because it had been previously inferenced that nodes T and C would be connected via path T-- X-- Z-- C, the system would trace only need to undo the inference that LINK A-- B was up and that path T-- A-- B-- C could be used as a path between nodes T and C. Thus by starting the undo consequences at the inference iteration using the fact LINK A-- B to be up and by simply undoing those consequences, the system would trace back to the inference finding another alternative node path, namely, LINK T-- X, LINK X-- Z and LINK Z-- C, to exist. The redo step would simply reassert the consequence of eliminating the alternate path from nodes T to C using LINK A-- B is a useable path. Thus by concentrating the analysis on the immediate consequences of LINK A-- B being down and using previously asserted consequences that are not affected by LINK A-- B being down, namely that nodes T-- C could be connected through an alternative path, the system can be more efficiently operated than if the system would have to be started anew or if some complex tree structure had to be retraced or recalculated.
A more detailed explanation of the present invention will be provided in the context of the language DATALOG *, wherein an application of the present invention is provided. The reason for choosing this language is that it has several attractive characteristics. First, it is set-oriented, a typical feature of database rule languages. Second, it is sufficiently rich, allowing negation in the body and in the head of rules. Third, it is abstract enough to enable identifying principal concepts that relate to incremental evaluation of rules, independently of the idiosyncrasies of commercial languages.
The Language DATALOG *
In this section, the basic terminology for DATALOG * is introduced. Intuitively, rules in DATALOG * have the general form:
�h(. . . ):-�b1 (. . . ), . . . ,�bk (. . . ), . . . �bn (. . . )
A positive head implies the corresponding fact should be added to the database on rule firing, whereas a negative head implies the corresponding fact should be removed from the database, if it exists.
Formally, DATALOG * programs are built from atoms, which are predicate symbols followed by a list of arguments. The arguments may be either variables or constants. For simplicity, constants are natural numbers. A literal is either an atom, also called a positive literal, or a negated atom, also called a negative literal. If all arguments are constants, we call the literal a fact. A rule consists of a literal, the head of the rule, and possibly a conjunction of positive and negative literals which form the body of the rule. Variables are denoted by capital letters, and predicate names are strings built from lower case letters. An example of a rule is
a(X,Y):-a(X,Z),p(Z,Y), q(Z,2)
where the head of the rule is always to the left of :-, and the body to the right. If Q is a set of facts, then Q denotes the set having the same literals as Q, but with their sign reversed.
A program is a set of rules. A program is called safe if each rule fulfills the following conditions:
(1) Each variable in the head of the rule also occurs in the body of the rule, and (2) Each variable in a negative literal in the body also occurs in a positive literal of the body. We require that programs be safe.
The instantiation of a rule r is defined with respect to a database, i.e. a finite set of positive facts; it is an assignment of constants to the variables in r such that all the positive facts in the body are in the database, and the negative ones are not.
The operational semantics of the evaluation are as follows. The input to a program is a database. The output of a program P for an input I, denoted O(P,I), is the database obtained at the end of the following iterative procedure.
______________________________________Procedure DATALOG *-- SEMANTICS1. Start with the database consisting of the input. Initialize the evaluation iteration count IC ← 0.2. (Match) Determine S, the set of operations each of which is the head of an instantiation with respect to the current database. If this set is empty, stop.3. (Select) Let S' be the subset of S consisting of all the operations whose negationis not in S (i.e., if f and f are bothin S then neither operation is in S').4. (Act) Execute the operations in S'. Increment IC by 1. Return to 2.end {procedure DATALOG *-- SEMANTICS}Notice that the Select step indicates that if a fact f isadded and deleted at the same iteration, regardless of thenumber of occurrences of these operations, the status of f(In or Out of the database) remains as it was in theprevious iteration.______________________________________
Incremental Evaluation Update Procedure
Basically, the inference procedure of the present invention consists of two phases: DO and INCR-- UPDATE. The DO phase consists of the procedure DATALOG * -- SEMANTICS of the previous section. After the Act step, there is a check to determine whether an interrupt message has been received. If so, the INCR-- UPDATE procedure is invoked. The incremental evaluation update stage consists of an UNDO phase and a REDO phase. The interrupt message consists of a set Q, of positive and negative facts, denoting additions and deletions, respectively, to be applied to the input database.
Consider the DO phase. With each fact f that was in the database at some iteration, we associate a fact chain that enables us to determine the status (in or out of the database) of f at each iteration. The structure of the fact chains will be described infra.
Suppose that the positive fact f is in Q, and f was not in the input database. Then we first execute the match step of the inference procedure, considering only instantiations with respect to the input database in which f appears in the body of the instantiated rule. This is the UNDO phase. Let U be the set of operations in the heads of these instantiations. Each operation in U will have to be undone. Second, we execute the match step of the inference procedure, considering only instantiations with respect to the input database, in which f appears in the body of the instantiated rule. This is the REDO phase. Let R be the set of operations in the heads of these instantiations. Each operation in R will have to be redone.
Now suppose that +e (or simply, e) is in R, indicating that the fact e would have been added to the database in the first cycle, had f been in the input database. This does not necessarily mean that the incremental procedure should repeat the redo phase with e. It is possible that in the first cycle e was added to the database not only as a result of the instantiation that has f in the body, but also as a result of another instantiation. Then e is not new in the second iteration, and it should be eliminated from the incremental evaluation of the subsequent cycles. In other words, based on the sets U and R we should determine which facts are new. Only they should be carried forward, to the next cycle in the incremental evaluation.
Generally speaking, given UNDO and REDO sets, the incremental procedure, call INCR-- UPDATE, determines which facts must be viewed as having changed their status as a result of the incremental procedure at the specified iteration. The set of facts that are determined to have changed their status is called the NEW set. This set is the focus of interest of procedure INCR-- UPDATE. The procedure recursively redoes all the inferences that descend from the NEW set, and it also recursively undoes the inferences that stem from NEW. In other words, UNDO and REDO sets are generated for successive iterations, given the initial ones. Formally, procedure INCR-- UPDATE is as follows.
recursive procedure INCR-- UPDATE (U,R,i)
Comment: This procedure is invoked by the inference system, as an exception handler on receiving incremental modifications to past assumptions made on the database. The procedure modifies the database, given two sets of operations, U and R, that have to be incorporated at some past evaluation iteration i. Each set contains positive and negative facts. The set U represents a collection of facts whose addition or deletion (depending on the sign) has to be undone at iteration i. It is called the undo set. The set R represents a collection of facts whose addition or deletion has to be incorporated at iteration i. It is called the redo set.
______________________________________Current Evaluation Iteration Number in the DO Phase.2. If (i = η) RETURN to the DO Phase.3. For each fu ε Ucall UNDO-- MAINTAIN-- CHAIN(fu, i) Comment: undoing an operation amounts to the manipulation of the corresponding fact chain.4. For each fr ε Rcall REDO-- MAINTAIN-- CHAIN(fr, i) Comment: redoing an operation amounts to the manipulation of the corresponding fact chain.5. NEW←COMPUTE-- NEW(i) Comment: the NEW set is computed by examining the fact chains.6. If (NEW = φ) RETURN to the DO Phase.7. Find all rule instantiations with respect to the database at iteration i, such that a fact fr ε NEW is in the body. Denote Fr the set of facts in the heads of these instantiations. This will serve as the redo set for the recursive call.8. Find all rule instantiations with respect to the database at iteration i, such that a fact fu ε NEW is in the body. Denote Fu the set of facts in the heads of these instantiations. This will serve as the undo set for the recursive call.9. Call INCR-- UPDATE(Fu,Fr, i + 1). end {procedure INCR-- UPDATE}______________________________________
The initial invocation is INCR-- UPDATE(φ,Q,0), where Q is the set of facts in the interrupt message.
The computations in steps (7) and (8) of the above recursive procedure is clearly less expensive, in general, than computing all instantiations of rules at iteration i.
In order to demonstrate the correctness of the INCR-- UPDATE procedure, we need to show that if an incremental update message to incorporate a positive or negative fact f in the input database is received while the processor is at iteration t>0, then the final database produced would be the same as in the case where f is in the input database.
As mentioned earlier, for each fact f there is a chain C(f) that enables the system to determine the status of f at each iteration. For each iteration in which f was added or deleted (either in the DO or the REDO phases) we keep a count of the number of times it was added, and the number of times it was deleted. In other words, for each iteration we keep a state record in C(f), which has the structure illustrated in FIG. 4.
Fact chains are different fundamentally than timestamps, typically used in concurrent data base update applications. Fact chains determine the status of facts in or out of the database for each iteration of an iterative inferential process. Timestamps, see Ullman, are used to label individual transactions with a unique time that a transaction was initiated. Timestamps are thus used to ensure concurrency control of multiple concurrent updates to a database. It is assured that no two transactions have the same timestamp thus ensuring that multiple transactions are performed in a serial order. Fact chains, on the other hand, are associated with the data records in the database, not the transactions performed against the data, and indicate the status of a fact at some inference iteration cycle.
In FIG. 4, the Iter field indicates which iteration a particular state record corresponds to, and the Status field holds the status of the fact (In or Out of the database) at that iteration. The Added and Deleted fields are necessary to count the number of occurrences of additions and deletions (even though the operational semantics do not require so), since one does not know how many of these operations will be undone in the future.
Each fact present in the initial input database is called an initial fact. We set up fact chains for the initial facts at startup time. All such facts have fact chains initialized with a single state record s, with s.Iter set to 0, and s.Status set to In. Additional chains are established for those facts which are added or deleted during inference, and for which no chain was established initially. Chains established during inference consist of a state record at iteration 0 with a status of Out, and a state record for iteration t, where t is the iteration when f was added or deleted during the inference process.
Maintenance operations on chains occur in all phases of incremental evaluation. DO and REDO phases increment the count in the Added or Deleted fields of the state record, depending on whether the fact in question is added or deleted as a result of rule firing (note that facts are never physically deleted, only their delete count is incremented). In contrast, UNDO decrements the appropriate field. The procedure that manipulates the chains during DO and REDO phases is REDO-- MAINTAIN-- CHAIN. Procedure UNDO-- MAINTAIN-- CHAIN manipulates chains during the UNDO phase. REDO-- MAINTAIN-- CHAIN receives a set of facts and an iteration number, and increments the counts of the appropriate fields of the state record indicated by the iteration number, whereas UNDO-- MAINTAIN-- CHAIN decrements the counts. As an example, consider the effect of the procedure call
______________________________________UNDO-- MAINTAIN-- CHAIN({f1, f2 }, i).Suppose C(f1) has a state record u with u.Iter=i,u.Added = 1 and u.Deleted = 1, and C(f2) has a state recordv with v.Iter=i, v.Added = 0 and v.Deleted = 1. After theprocedure executes, we will have u.Added = 0, u.Deleted = 1,v.Added = 0, v.Deleted = 0. Subsequent to this, if we makethe callREDO-- MAINTAIN-- CHAIN({ f1, f2 }, i)we will end up with u.Added = 0, u.Deleted = 2, v.Added = 1,v.Deleted = 0.______________________________________
The status of a fact f for any iteration is determined by examining the Added and Deleted fields in the state record for that iteration. This applies to the DO phase and to the incremental update phase as well. In procedure INCR-- UPDATE, the final value of the status of a fact f and the set of new facts at an iteration is computed with the COMPUTE-- NEW function.
COMPUTE-- NEW takes an evaluation iteration number i as argument, and scans the chains for the facts that were changed during the UNDO or REDO phases. If both the Added and Deleted fields are zero in the state record for i, or if both are non-zero, then we revert to the status at iteration i--1. Otherwise, if the Deleted field is 0, we interpret that as indicating that the fact was only added, and the status is set to In, whereas if the Added field is 0, the status is set to Out. A fact is flagged new if its newly computed status is the inverse of its former status. The outline of procedures INITIALIZE-- CHAINS, UNDO-- MAINTAIN-- CHAIN, REDO-- MAINTAIN -- CHAIN, and COMPUTE-- NEW are shown below.
______________________________________PSEUDOCODE FOR MAINTENANCEOF FACT CHAINS______________________________________procedure INITIALIZE-- CHAINSDB ← Global DatabaseFor Each F ε DB doCreate a new chain C(F) indexed by F.Link a state record s to C(F). Set the fields of s asfollows:s.Added ← 1s.Deleted ← 0s.Iter ← 0s.Status ← Inend {procedure INITIALIZE-- CHAINS}procedure REDO-- MAINTAIN-- CHAIN(f,i)F ←|f|DB ← Global DatabaseIf C(F) existsIf ( s ε C(F) such that s.Iter = i, thenif f is positive, set s.Added ← s.Added + 1else if f is negative, set s.Deleted ← s.Deleted + 1ElseInsert a new state record p at the correct position in the chain.p.Iter ← iif f is positive, set p.Added ← 1,p.Deleted ← 0else p.Deleted ← 1, p.Added ← 0.Else if there is no C(F) thenCreate C(F). Link initial record s to it.Set s.Added, s.Deleted, s.Iter fields to 0, and s.Statusto Out.Add a record to the end of the chain, or overwrite theinitial record if i = 0. Call this record p.Set p.Iter ← iIf f is positive then p.Added ← 1, p.Deleted ← 0,DB ← DB ∪ {f}else if f is negative than p.Added ← 0, p.Deleted ← 1end {procedure REDO-- MAINTAIN-- CHAIN}procedure UNDO-- MAINTAIN-- CHAIN (f,i)F ← |f|Find the state record s in C(F) with s.Iter = iComment: cancel adds or deletes from some previousiteration.If f is positive then s.Added ← s.Added - 1else s.Deleted ← s.Deleted - 1end {procedure UNDO-- MAINTAIN-- CHAIN}function COMPUTE-- NEW(i)NewSet ← φFor each fact chain that has been updatedFind the state record s at iteration i if it existsif ((s.Added = 0) (s.Deleted = 0)) then s.Status ← Status at (i - 1)else if ((s.Added ≠ 0) (s.Deleted ≠ 0)) then s.Status ← Status at (i - 1)else if (s.Deleted = 0) then s.Status ← Inelse if (s.Added = 0) then s.Status ← OutIf the new status represents an inversion, then f ← Fact under consideration, with sign. NewSet ← NewSet ∪ {f}RETURN (NewSet)end {functionCOMPUTE-- NEW}______________________________________
For stratified DATALOG, the rule program interpreter evaluates the rules one stratum at a time. (For a general discussion relating to stratified DATALOG See Ullman, supra). Iteration numbers are then replaced by the pair (Stratum-- Number, Iter-- Number), which may be regarded as our new iteration counter. This adaptation of this implementation calls for the adoption of a modified numbering scheme for chain maintenance operations, and for matching rules in steps (7) and (8) of procedure INCR-- UPDATE. First, instead of ordering the fact chains by iteration number, we now use the order imposed by lexicographic ordering of the (Stratum-- Number, Iter-- Number) pairs. Therefore, in the incremental update phase, instead of referring to iteration i--1, we refer to the previous iteration in the lexicographic order. Second, in steps (7) and (8) of procedure INCR-- UPDATE, we restrict the matches to rules in the particular stratum being considered. With these simple modifications, INCR-- UPDATE is applicable to stratified DATALOG.
In a data reduction paradigm for DATALOG (without negation), instantiations of the rules in a given program are partitioned among a set of processors. The original program is evaluated on each processor, but with less data. The partition of the instantiations is achieved by restricted versions of the original program, such that each restricted version is evaluated at one processor. In general, it is necessary for the processors to communicate intermediate results to each other by message passing. This paradigm is extended to DATALOG *. In particular, we discuss a synchronous variation of this extension, whereas in the next section, we discuss an asynchronous one.
Formally, let P={r1, . . . rm } be a program with m rules and {P0, . . . , Pk-1 } be a set of k>1 processors. For each rule ri, we designate k restricting predicates, hij (X1, . . . Xqi), for 0≦j≦k-1. The arguments X1 . . . , Xqi are the same for all the k predicates, and by definition, all the arguments are variables of ri. We require that for each instantiation of the variables X1, . . . , Xqi, the predicate hij is true for exactly one j. Denote by rij the restricted version of the rule ri having the restricting predicate hij (X1, . . . , Xqi) appended to its body. Denote by pj the restricted version of P consisting of the set of rules {rij |1≦i≦m}. The set {P0 , . . . , Pk-1 } is called a data reduction parallelization strategy for P.
The set of processors {P0, . . . , Pk-1 } cooperate in evaluating P in parallel as follows. The database is partitioned among the processors such that each tuple has a unique Data Handler processor (DH) at which it resides. The DH processor for each tuple is computed by a hash function associated with the relation. For example, the tuple a(X,Y) such that h(X)=i resides at processor i.
In the course of the evaluation, a tuple may be added and deleted by multiple processors. In databases, concurrency control is concerned with guaranteeing that the result of all transactions is equivalent to some serial execution of those transactions. In our case, we can view the pi 's as executing separate transactions, where a transaction consists of the actions executed by Pi at a given iteration. However, it is not enough to provide standard concurrency control on accesses to the distributed database, as some serial executions may not be acceptable according to the semantics. It becomes necessary to provide some means to act as an arbiter over database adds and deletes. This is realized by the Data Handler for each tuple. One can think of a data handler as a monitor program that runs on each processor that stores tuples and sequentially services requests for tuples by other processors. The data handlers are responsible for realizing the correct semantics with respect to database updates, as will be demonstrated below. For this variation, fact chains are not necessary.
Restricted versions of the rules are distributed to the processors, as described earlier. These are called the Rule Handlers (RH) for program P. When a rule handler requires a fact for matching, it requests it from the appropriate data handler. Determining the appropriate data handler involves a trivial evaluation of the associated hash function.
Each processor Pi performs the instantiations of Pi as in the normal evaluation procedure. Any newly inferred facts are transmitted to the appropriate data handlers for storage. All processors execute in step with respect to the evaluation iteration number, i.e., all processors have the same iteration number, which is used for synchronization. The following procedure is executed by Rule Handler i at iteration j, in the synchronous variation of the data reduction paradigm.
______________________________________procedure SYNC-- DATA-- REDUCTIONAt iteration j do:1. Request facts from appropriate Data Handlers for the rule instantiations of pi withrespect to the database at iteration j.2. Add or delete tuples, as indicated by these instantiations. This is done bytransmitting the operations (i.e. additions ordeletions of facts) to the appropriate DataHandlers.3. When all the rule and data handlers have completed iteration j (as determined by somedistributed termination procedure, e.g. asdescribed in K. M. Chandy and J. Misra; "On Proofsof Distributed Algorithms with Applications to theProblem of Termination Detection"; Manuscript,Dept. of Computer Science, University of Texas,Austin; and N. Francez; "Distributed Termination";ACM Transactions on Programming Languages andSystems, Vol. 2, No. 1, pp. 42-55, 1980.pi moves to the next iteration.Data Handlers do the following sequence of steps:1. Receive all the messages from the rule handlers, indicating the operations atiteration j.2. Update local database as follows:If for a fact f, an add and a delete operation is received (possibly from different rule handlers) the status f at iteration j + 1 remains the same as the status of f at iteration j. This is obviously also the case if no operation on f is received. Otherwise, if only adds (deletes) are received, f is added to (deleted from) the database.end procedure {SYNC-- DATA-- REDUCTION}______________________________________
Increased efficiency is implied by the fact that pi evaluates a restricted version, meaning it does fewer instantiations compared to the case where the original program P is evaluated.
A major problem with the procedure described above is that no processor can proceed faster than the slowest one at each iteration. Obviously, we desire asynchronous operation of each Pi, but this gives rise to other problems which motivate the use of the INCR-- UPDATE procedure.
Suppose some processor Pi deletes a fact f at iteration t1, which was used earlier by processor pj. Suppose also that pj is now at iteration t2, and t2 >t1. Unless handled properly, this situation may cause the operational semantics of DATALOG * to be violated.
Recall that INCR-- UPDATE allows the incremental incorporation at iteration t2, database changes occurring at iteration t1 <t2. The message indicating deletion of f by Pi in the example above can be viewed as an interrupt message. Thus, by using the procedure INCR-- UPDATE, pj can update its database to a consistent state.
We outline below the asynchronous variation of the data reduction paradigm (henceforth called ASYNC-- DATA-- REDUCTION) for parallel and distributed evaluation of DATALOG * programs. As before, we have rule handlers that own restricted versions of the rules of the original program. Furthermore, when the database is partitioned among the processors, each data handler maintains a chain for each fact. The processors operate asynchronously and each rule handler keeps its own iteration count.
More precisely, in ASYNC-- DATA-- REDUCTION each rule handler, RHi, executes steps (1) and (2) as in SYNC-- DATA-- REDUCTION, at each iteration j. At step (3), instead of synchronizing, RHi checks whether or not it received an interrupt message from a data handler; such a message contains a set of NEW (positive and negative) facts at an iteration number. If this is not the case, RHi simply proceeds to the next iteration. If the message pertains to an iteration number higher than j, it is ignored and RHi proceeds to the next iteration.
Otherwise, RHi executes steps (7) and (8) of INCR-- UPDATE for each interrupt message, in increasing iteration number order (meaning that it will not process a NEW set for iteration 500 if a NEW set for iteration 400 is in the queue). RHi sends the resulting UNDO and REDO sets to the data handlers (note that each set will be partitioned, with each partition being sent to a different data handler). After processing all interrupt messages RHi proceeds to iteration j+1.
Each DHi continuously receives REDO and UNDO sets, each associated with an iteration number. DHi processes these in increasing iteration number order. For each UNDO (REDO) set associated with an iteration, say k, DHi performs UNDO-- MAINTAIN-- CHAIN (REDO-- MAINTAIN-- CHAIN), and COMPUTE-- NEW. The resulting NEW set and the iteration number k are broadcast to the RH's. If both an undo and a redo set exist for k, the UNDO-- MAINTAIN-- CHAIN and REDO-- MAINTAIN-- CHAIN will both be performed before COMPUTE-- NEW. A possible implementation for the above system is shown in FIG. 5.
It should be clear that many optimizations of the transmission sets are possible, as discussed in O. Wolfson and A. Ozeri; "A New Paradigm for Parallel and Distributed Rule-Processing"; Proceedings of the ACM-SIGMOD 1990. International Conference on Management of Data, Atlantic City, N.J., May 1990.
The procedures described herein can be used for two seemingly unrelated purposes: first, to maintain the inference database in an incremental fashion in a dynamic environment where the input may be updated after inferencing is under way, and second, for the asynchronous version of the data reduction paradigm.
The INCR-- UPDATE procedure can be implemented in a communication network management system and other systems in which incremental update capabilities are necessary in a real time environment.
The incremental update processing procedure herein described may also be effectively used in combination with meta-level conflict resolution rules, referred to as Meta Redaction Rules. These rules regard the set of instantiations at each cycle as working memory, and remove from the set of instantiations those members that are considered to be conflicting instantiations according to these rules. After redaction of the conflicting instantiations, all the remaining instantiations in the conflict set can be fired in parallel. The meta-redaction rules seek to prevent conflicts which may subsequently develop and enhance the operation of any system by avoiding future conflicts in the instructions resulting in subsequent rule firings. Thus, the meta redaction rule may be defined to provide a means to check for conflicts between multiple instantiations of the same rule in a parallel application.
An example of the syntax defining a meta redaction rule is as follows:
______________________________________ (m <meta-rule-name> (<conflicting-instance-1>) (<conflicting-instance-n>) {<arith-preds>}→ (redact <redaction-instance-1> . . . <redaction-instance-m>))______________________________________
In the above example, the LHS of a redaction rule consists of the symbol m, followed by the meta-rule name, followed by specifications of rule instances in the conflict set and an arbitrary arithmetic predicate applied to variables bound in the specification of the instances. The conflicting rule instances are chosen such that if all those instances are present in the conflict set and the arithmetic predicate holds, one or more of the instances must be redacted. The RHS of a redaction rule consists of the specification of those instances that must be redacted from the conflict set.
Although the procedures and components listed above represent an embodiment of the present invention, it will be appreciated by those skilled in the art that many modifications could be made to the procedures and components without modifying from the spirit of the present invention For instance, the procedures could be modified for implementation in other rule-based languages. In addition, the state record could be modified and maintained in a number of different ways to provide the same basic function of producing a fact chain without the need for a dependency graph.
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|U.S. Classification
|706/45, 706/60
|International Classification
|Cooperative Classification
|European Classification
|Sep 13, 1991
Owner name: TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW
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text:
Odavad lennud alates Gibraltar (riik)Kas Te pidasite silmas Lennud sihtkohta Gibraltar (riik)?
Odavad lennud Gibraltar (riik)-sse ainult läbi Bookinghouse.ee! Our sophisticated search system will find the best flight deals from dozens of different airlines, such as Ryanair, WizzAir and many others. Bookinghouse.ee is fast and easy to use. But most importantly, it's completely free!
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Hawk Incentives
This article has multiple issues. Please help improve it or discuss these issues on the talk page. (Learn how and when to remove these template messages)(Learn how and when to remove this template message)
Hawk Incentives (formerly Blackhawk Engagement Solutions, Parago, and InteliSpend), is a rewards-based incentives company that designs, implements and manages programs for other companies. Hawk Incentives is based in Texas.
Hawk Incentives has become one of the larger providers of manufacturer and retailer promotional marketing programs, including rebates and incentives programs. The Company has been a major player in the popularization of paperless rebates and prepaid rebate card programs.[1] In 2009 the company released research showing that 83% of consumers were seeking products with rebates.[6] In addition, the company has received significant industry recognition for its research into customer loyalty, rewards programs and prepaid cards. In 2009, Parago received an A+ rating from the Dallas Better Business Bureau.[2] However, as of April 2015, the BBB has received over 200 complaints against Parago and the rating has been lowered to B+. [3]
Some of Hawk Incentive's clients include Netgear, Sprint Nextel and Staples Inc.. For Staples, Hawk Incentives is credited with redesigning and managing the popular Easy Rebates program, which enables customers to use paperless submission for rebates, offers a choice of rewards and quick payment. After a decade of working together, Staples extended its contract with Parago for another five years in 2010.[7]
President & CEO Juli Spottiswood serves as a board member and Treasurer of the Network Branded Prepaid Card Association (NBPCA), a nonprofit organization that promotes consumer education to help consumers optimize their experiences with network branded prepaid cards, develops recommended best practices for the industry and provides a fact-based voice to media, government and consumers. [8]
The firm has experienced significant growth and expansion in the early 2000s. In 2010, Parago was named one of the fastest growing women-owned or led businesses in North America, as judged by the Women President's Organization.[9] The same year, Parago ranked number 19 on PROMO magazine's 2010 PROMO 100 list of the top U.S. promotion agencies [10] and Spottiswood was named one of the Top 25 Industry Leaders by the Dallas Business Journal's Women in Business Awards. [11] In September, 2014, Parago was purchased by publicly traded Blackhawk Network, a leading prepaid and payments network, for $290 million. [4]
In 2009, Parago President and CEO Juli Spottiswood was recognized as the Ernst & Young Entrepreneur Of The Year 2009 in the business services category in Southwest Area- North.[5] The same year, the Company was selected as one of the best places to work in Dallas, through the Dallas Business Journal awards program.[6]
In 2004, Parago published research on customer loyalty and referral which appeared in the book Strategic Database Marketing, 3rd Edition by Arthur Middleton Hughes of KBM.
The Company was founded in 1999 as the "how to" website How2.com. In 2001, the company shifted its business to rebate and reward programs. Parago was founded by venture capital firm T. H. Lee of New York and was formerly known by the name Parago Rebates HQ.
- ^ [1], Reuters: Parago Grows 20 Percent in 2008; Achieves Significant Milestones
- ^ [2][permanent dead link], Yahoo! Finance: Parago Garners A+ Rating From Dallas Better Business Bureau
- ^ [3], Better Business Bureau, Dallas and Northeast Texas
- ^ [4], Blackhawk Network Announces Agreement to Acquire Parago, Inc.
- ^ "Archived copy". Archived from the original on 2009-09-29. Retrieved 2009-09-01., Ernst & Young: Winners for Ernst & Young Entrepreneur Of The Year 2009 awards in Southwest Area-North announced
- ^ [5], Entrepreneur.com: Parago Named One of Best Places to Work in Dallas


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San Judas Group Home
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I visited this facility
I have visited San Judas Group Home many times. In general,  I though San Judas was a good home for the clients that they take. The curb appeal is not the greatest. The house is located down a cul de sac street and at the end of the street there's a ditch that is an eyesore. The community is not a great one but you can see the changes as the city starts to rebuild the surrounding areas. The backyard was simple but cute and did its purpose. The common areas of the facility has that homelike feel to it, the living room is nicely decorated, the den has nice cozy furniture with a nice entertainment area for residents to enjoy movies, but the lighting is dim. The residents' rooms are a good size but are pretty bland. The staff was ok, they assisted the residents if they needed them too. I never really met the management so I can't speak on that behalf. While I was there, I noticed the residents were washing the dishes, doing laundry, and cleaning. The residents they have there all fit and are all Veterans Overall, San Judas Care Home is a nice home and I wouldn't recommend it to everyone but I would recommend it to Veterans and anybody who has a dad or grandpa that needs a home in the South Phoenix area and he is pretty mobile this would be a great home. I give this home four stars because San Judas has their niche down and the know what kind of residents they are experienced to take care of.
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The Writer's Center cultivates the creation, publication, presentation, and dissemination of literary work. We are an independent literary organization with a global reach, rooted in a dynamic local community of writers. We believe the craft of writing is open to people of all backgrounds and ages. Writing is interdisciplinary and it is unique among the arts for its ability to touch on all aspects of the human experience. Writing and literature enrich life and open doors to knowledge and understanding.
We currently serve the greater Washington, DC metropolitan area, with capacity to serve writers nationwide through online learning.
Stewart Moss
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How does this organization make a difference?
Self-reported by organization
In our 35 year history, we have helped develop the writing careers of Doreen Baingana, Pamela Ehrenberg, Barbara Esstman, Marita Golden, A. Van Jordan, Pagan Kennedy, Eugenia Kim, Alex MacLennan, Cate Marvin, James Mathews, Leslie Pietrzyk, Nani Power, Alex Sanchez, Rose Solari , Manil Suri, David Taylor and many more.
Self-reported by organization
What are the organization's current programs, how do they measure success, and who do the programs serve?
Program 1
WC is a place to have contact with readers, to provide training and development in the field, and to meet with the public as well as with each other. In essence, the Center is a literary crossroads for contemporary writing. WC offered a variety of workshops in all genres, for beginners as well as publishing writers, in which they may prepare work for publication and presentation. It also provided a Book Gallery to show and disseminate regional writers and small presses. WC is the only organization in the region to provide the total, affordable, interrelated system of public programs designed to support writers and the literary arts, and has distributed over $450,000 in small press books.
Program long term success
Offered 270 workshops in writing of all genres, to all ages.
Program success monitored by
Not available
Program success examples
Not available
Population(s) Served
Program 2
Emerging Writer Fellowship
In fall 2009, we established a new ongoing program that brings together emerging writers from across North America with a local band from the DC area for a night of literature and music. Each emerging writer is selected to receive a fellowship from a competitive pool of applicants; funding is provided to assist them with travel expenses. The Fellows are selected by an impartial committee of board members, community members, and workshop leaders who evaluate their creative work twice annually.
The Fellowships and the events themselves received significant free promotion among social networks like Facebook and regional and national blogs that promoted the opportunity to writers and audiences. The events drew some of our largest audiences of the year and introduced The Writer's Center to a new generation of writers and readers.
Program long term success
Not available
Program success monitored by
Not available
Program success examples
Not available
Arts, Culture & Humanities
Population(s) Served
General Public/Unspecified
We currently serve the greater Washington, DC metropolitan area, with capacity to serve writers nationwide through online learning.
External Reviews
Source: greatnonprofits.org
The review section is powered by Great Nonprofits
The people, governance practices, and partners that make the organization tick.
Stewart Moss
Stewart has worked for more than thirty years as an educator and fundraiser in independent schools around the country. An avid poet, he has taught literature and creative writing in both the USA and abroad. Scotland, Greece, Zimbabwe, Afghanistan, and Nepal are among the countries in which he has lived and worked. A native of Boston, MA, Moss was educated at Union College (NY) and Harvard University, where he studied the relationship between culture and religion. He lives in Annapolis with his wife, the writer Barbara Klein Moss.
Mier Wolf
Term: Sept 2008 - July 2010
GuideStar worked with BoardSource, the national leader in nonprofit board leadership and governance, to create this section, which enables organizations and donors to transparently share information about essential board leadership practices. Self-reported by organization
Does the board conduct a formal orientation for new board members and require all board members to sign a written agreement regarding their roles, responsibilities, and expectations?
Has the board conducted a formal, written assessment of the chief executive within the past year?
Have the board and senior staff reviewed the conflict-of-interest policy and completed and signed disclosure statements in the past year?
Does the board ensure an inclusive board member recruitment process that results in diversity of thought and leadership?
Has the board conducted a formal, written self-assessment of its performance within the past three years?


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Walk to your quarters to deliberate for a moment. The watch is lost for the time being, but something may be gained by taking a moment to prepare before your next encounter.
Taraq wound his way through the subterranean darkness of the stronghold. He absently thrust his hand into his jacket pocket in search of the cold comfort of the watch, but it wasn't there.
No comfort to be found.
He turned the corner and walked down another hallway, ever darker, ever downward. He grit his teeth in frustration and slammed his fist into the slimy limestone wall to his right. How could he have been so careless? He'd had that watch since before -- everything.
Safely sequestered deep beneath the city, far from the sun's deadly reach, Taraq's impeccable night-vision led him to his room, where he entered and closed the door soundlessly. He sat heavily on his cot and threw his coat to the floor, kicked off his boots and put his hands to his eyes. In another life he might have cried, but there are no tears for the Nightwalker, so he remained motionless at the edge of his mattress and let the rage take over.
Vampires did not cry, but they did feel rage.
The rage which now threatened to consume him, to rise up and take control, a maelstrom of crimson colors that boiled up from his stomach. It made him so angry that his body quivered with the urge to do harm.
He put his hand into his empty pocket, felt for the watch, grit his teeth all the harder.
His quest for power had been an all-consuming passion of his for many years, yet his spiraling emotions made his difficult plans nearly unattainable. He couldn't lose control, not when he was so close.
Taraq leaned back so that the back of his head touched the cool wall and took a deep breath to calm himself. He pulled his hands away from his face and studied them in his pitch black room. After a moment, the nervous jangle of his fingers ceased and the tightness in his chest dissipated. He had once again defied his rage and had taken control of himself.
Memories, so hard to come by these days, flooded the space behind his eyes. They were of occurrences and events of so long ago they were no longer as vivid as they once had been, and though he tried to reach out for them and pull them in they seemed just out of reach. Just far enough to remain blurred, or darkened. He squinted to improve his sight of those times, but it was not his eyesight that was lacking. It was his heart.
They were images of a tall, dark woman with braided hair down to her lower back and a smile that lit up the world: Cayten. She was holding a boy of five or six in her arms, swinging him merrily into the air. Taraq could not see the boy's face, but he was certain the boy looked just like him: a slim build, long face, dark eyes. And though Taraq was no longer able to see the boy's face after so many centuries, for some reason his son's laughter was sharp and clear, almost echoing off the walls surrounding Taraq. A light scent of Chrysanthemum wafted to Taraq's nostrils. Gentle and soft, from long ago, but with enough power to knock the breath from his lungs.
The visions instantly turned to ones of fire. Taraq pulled his covers around him to ward off the inferno as it engulfed him. The sounds of his child's laughter replaced by the boy's terrified screams as the fire seared his little body. Try as he might, Taraq could find neither the boy nor his beloved Cayten. The heat burned his eyes and hair, but still he pushed on through the flames to save his family. Something held him back, and all he could do was scream as smoke filled his lungs.
Alone in the darkness Taraq felt the anguish consume him, and he pressed his palms into his burning eyes. Long, tearless, and pitiful sobs wracked his figure, yet his eyes remained as dry as papyrus.


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BizStarts starts forums to hear funding pitches
BizStarts Milwaukee has launched an effort to bring together the region's investors and very young companies looking to raise funding.
BizStarts Investor Forum held its first meeting this month at the downtown law offices of Whyte Hirschboeck Dudek. Four start-ups that went through BizStarts' Venture Track Accelerator program made presentations for about 45 investors, said Dan Steininger, president of BizStarts.
"Now we have a central place for everybody to look at the deals that are coming out of the region. We have a place to funnel our deals and come together in one spot," Steininger said.
BizStarts is one of many groups working to help entrepreneurs. Gener8tor and VET ransfer's VictorySpark accelerator have been training entrepreneurs for several years through formal programs and events where their companies make presentations to investors.
"There are a lot of efforts under way in the city to support entrepreneurs and provide funding to them, and we expect to see many more come online in 2013," said Greg Meier, managing director of Victory Spark.
BizStarts also has signed memorandums of understanding with the Medical College of Wisconsin, the University of Wisconsin-Milwaukee Research Foundation and the Milwaukee Water Council to take entrepreneurs from those organizations through classes and connect them with investors.
Victory Spark is also working with entrepreneurs affiliated with UWM, the Water Council and other local organizations, Meier said.
The 2012 State New Economy Index, which measures how well states innovate, create knowledge jobs and foster globalization, ranks Wisconsin 31st among all states.
Milwaukee lags even that middling performance. Just 26 companies in the Milwaukee area have been certified as qualified businesses by state Commerce officials, vs. 64 companies in Dane County. The certification is given to very young companies with high growth potential. It makes financial backers of the companies eligible for a 25% tax credit on the amount they invest in the company.
"BizStarts is trying to focus our energies and attention on creating an innovative economy for entrepreneurs," said Paul Eberle, chief executive of Whyte Hirschboeck and a member of the Golden Angels investing group.
Future BizStarts forums will be sponsored by other firms, Steininger said. The events will be open by invitation to qualified investors.
Business Wire
Recent national business updates from the Associated Press


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Hello world.
I never believed I'd do one of these journals, but hey, here's an opportunity, so here we go. Cakes are there to be eaten.
Starting tonight, in between my usual Sci-Fi Uploads, I'll be publishing detail excerpts from a large-scale digital painting I did for a pair of friends' On-Air-Design Bachelor of Arts thesis movie in 2009/2010, eventually posting the whole thing too. It was featured in the introductionary narration of their contemporary/occult fantasy short film and laid out in a way for the virtual camera to follow it's lenght as a visual representation of the backstory. It's quite a bit off my genre chart to say the least.
So, why is some old unpaid artwork such a big deal when most of my not-too-recent stuff is stashed away in my scraps gallery? First of all, I only recently rediscovered the whole thing (ironically so, considering the fact that the project was called "Finding"). Secondly, I realized that I like it comparatively well- a lot, even. I really took my time and mobilized a lot of effort and creativity for it back then, and it shows in the level of detail done in a higher resolution that I usually do (to allow for panning, zooming, and so on). Not a masterpiece by far, but I enjoy it to this day. Thirdly, it really offers itself to be cut apart and made into a gallery section of its own, so here's another cake, and I'll eat it too.
Have a great weekend,


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