Jayen Vaghani, Kotagiri Ramamohanarao, David B. Kemp, Zoltan Somogyi, Peter J. Stuckey, Tim S. Leask, and James Harland. The Aditi deductive database system. VLDB Journal, 3:245--288, 1994.  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... of C (the language in which the runtime system of Mercury is written) to store pointers to CallSiteDynamic structures and to The fourth kind of higher order call supported by the Mercury compiler, calling a procedure implemented in the Aditi2 deductive database system (the successor of Aditi [107]) is not supported by the Mercury profiler, since that would require profiling the C code of Aditi2 itself. list nodes in di#erent elements of the same array. Each element of the ps call sites field of a ProcStatic structure has a component that specifies which kind of pointer the ....

Jayen Vaghani, Kotagiri Ramamohanarao, David B. Kemp, Zoltan Somogyi, Peter J. Stuckey, Tim S. Leask, and James Harland. The Aditi deductive database system. VLDB Journal: Very Large Data Bases, 3(2):245--288, April 1994. Electronic edition.

....the deductive database approach which integrates the logic programming and relational database techniques and provides more powerful query language to support inference of large amounts of data. However, most deductive database systems such as Nail [26] LOLA [12] Glue Nail [10] XSB [33] Aditi [37], LogicBase [13] and Declare SDS [16] only support flat relations which are found inappropriate for advanced applications. A few deductive database systems that support data with complex structures such as LDL [8] and CORAL [30] are only implemented as memory based systems and do not support ....

....system loads into memory the meta information about relations and rules. Then it uses one of three kinds of evaluation strategies to process the user query: 1) matching; 2) semi naive bottom up evaluation; 3) top down evaluation. Matching Most deductive database systems such as Aditi [37], LOLA [12] LDL [8] CORAL [30] etc, use magic set rewriting techniques to improve the performance of queries that involves rules. Basically, they focus on how to improve the evaluation speed by using the constants provided in the queries. There are several problems with this approach. First, if ....

J. Vaghani, K. Ramanohanarao, D. Kemp, Z. Somogyi, P. Stuckey, T. Leask, and J. Harland. The Aditi Deductive Database System. VLDB Journal, 3(2):245--288, 1994.

....databases are fully understood and there has been a great deal of research dealing with implementation issues, particularly in query optimization in the presence of recursive rules. This research has culminated in various experimental systems such as NAIL [12] glue NAIL [15] LDL [1, 14] Aditi [19], EKS V1 [20] CORAL [16] and Starburst SQL [13] the utility of which have been successfully demonstrated. Therefore, it is not unreasonable to assume that within the next decade, commercial systems with deductive capabilities will become available. In the presence of a large number of facts ....

Jayen Vaghani, Kotagiri Ramamohanarao, David Kemp, Zoltan Somogyi, and Peter Stuckey. The Aditi deductive database system. In Proceedings of the NACLP '90 Workshop on Deductive Database Systems, Available as TR-CS-90-14, Dept. of Computing and Information Sciences, Kansas State University, Manhattan, Kansas, 1990.

....for non rst normal form and recursive relations) and they are suited for application in which a large number of data must be accessed and complex queries must be supported. With respect to the operational semantics, most deductive database systems (for instance, DATALOG [19] CORAL [16] ADITI [20]) use bottom up evaluation instead of top down one like Prolog systems. The reason is that the bottomup approach allows to use a set at a time evaluation, i.e. it processes sets of goals, rather than proceeding one (sub) goal at a time, where operations like relational joins can be made for ....

J. Vaghani, K. Ramamohanarao, D. B. Kemp, Z. Somogyi, P. J. Stuckey, T. S. Leask, and J. Harland. The Aditi Deductive Database System. VLDB, 3(2):245{ 288, 1994.

....such as a typical Prolog program, it is common to only need one answer to the query. The author has been partially supported by the Spanish CICYT (project TIC 980445 C03 02 TREND) One consequence of this is that most deductive database systems (for instance, DATALOG [25] CORAL [20] ADITI [26]) use bottom up evaluation methods instead of top down one. Bottom up approach allows us to use set at a time evaluation, i.e. it processes sets of goals, rather than proceeding one (sub) goal at a time, where operations like relational joins can be made for disk resident data eciently. In this ....

J. Vaghani, K. Ramamohanarao, D. B. Kemp, Z. Somogyi, P. J. Stuckey, T. S. Leask, and J. Harland. The Aditi Deductive Database System. VLDB, 3(2):245{ 288, 1994.

....variable length subrecords. For instance, suppose the following complex term address(Name,City)which can be used in the base relation person(DNI,address (Name, City) Job) in order to structure the stored information. Most deductive database systems (for instance, DATALOG [20] CORAL [14] ADITI [21], LOLA [23] allow to handle negative literals, increasing their expressive power as query languages. The introduction of negation in logic programming (see [4] for a survey) and thus the study of semantic models for logic programs have been widely studied in the past being most relevant one the ....

J. Vaghani, K. Ramamohanarao, D. B. Kemp, Z. Somogyi, P. J. Stuckey, T. S. Leask, and J. Harland. The Aditi Deductive Database System. VLDB, 3(2):245-288, 1994.

....database systems provide the user with enough control to implement this, and other di#erential improvements previously discussed, by coding them into the program. For instance, the LDL users could use XY stratified programs for this purpose [28] similar programs can be used in other systems [25]. 8 Conclusion This paper has introduced a logic based approach for the design and implementation of greedy algorithms. In a nutshell, our design approach is as follows: i) formulate the all answer solution for the problem at hand (e.g. find all the costs 34 of all paths from a source node to ....

Vaghani J., K. Ramamohanarao, D. B. Kemp, Z. Somogyi, P. J. Stuckey, T. S. Leask, and J. Harland. The Aditi deductive database system. The VLDB Journal. Vol. 3(2), 245--288, 1994.

....It is shown that these layered programs have well defined and equivalent model based and procedural semantics. Other constructors can be expressed using membership and grouping, for example union, set difference, set intersection and set definition by enumeration. The second example is Aditi [39], which is a deductive database system developed at the University of Melbourne. Base relations in Aditi have fixed arity but the attributes do not have associated types so any ground term can appear in any attribute position of any relation. Each relation has a sequence of one or more attributes ....

J. Vaghani, K. Ramamohanarao, D.B. Kemp, Somogyi Z., Stuckey P.J., T.S. Least, and J. Harland. The aditi deductive database system. VLDB Journal, 3:245--288, 1994.

....can be compiled accordingly. For a predicate with n arguments, there are 2 n possible query forms to be specified. Some query forms are not allowed because of the limitation of evaluation strategies. The user cannot issue a query that has no corresponding query form. Similarly, in Aditi system [25], every relation must have one or more query forms (called modes) If a query can not match a mode declaration, it will be rejected. This is burdensome from the user s point of view. Aditi and CORAL also support top down approach. However, the mechanisms used in both systems are tuple at a time ....

J. Vaghani, K. Ramanohanarao, D.B. Kemp, Z. Somogyi, P.J. Stuckey, T.S. Leask, and J. Harland. The Aditi deductive database system. VLDB Journal, 3(2):245--288, April 1994.

....can be compiled accordingly. For a predicate with n arguments, there are 2 n possible query forms to be speci ed. Some query forms are not allowed because of the limitation of evaluation strategies. The user cannot issue a query that has no corresponding query form. Similarly, in Aditi system [29], every relation must have one or more query forms (called modes) If a query can not match a mode declaration, it will be rejected. This is burdensome from the user s point of view. Aditi and CORAL also support top down approach. However, the mechanisms used in both systems are tuple at a time ....

J. Vaghani, K. Ramanohanarao, D.B. Kemp, Z. Somogyi, P.J. Stuckey, T.S. Leask, and J. Harland. The Aditi Deductive Database System. VLDB Journal, 3(2):245-288, 1994.

....redundancy checking. Indeed, for rangerestricted programs, they have been proven to be asymptotically equivalent [10, 8] under certain assumptions. Despite these well known equivalences, magic style systems have traditionally differed from tabling systems. Magic style systems, such as Aditi [15], CORAL [7] and LDL [3] are built upon set at a time semi naive engines, while tabling systems, such as XSB [9] use a tuple at a time strategy that reflects their genesis in the logic programming community. Each class of systems has its advantages and disadvantages. Presently for in memory ....

J. Vaghani, K. Ramamohanarao, D.B. Kemp, Z. Somogyi, P.J. Stuckey, T.S. Leask, and J. Harland. The Aditi deductive database system. The VLDB Journal, 3(2):245-- 288, 1994.

....to be the use of N as mentioned above. This is a ner grained notion than in the classical case. There, as facts may be arbitrarily copied, all rules are independent, as it is possible to make as many copies as is needed to satisfy each rule. Moreover, deductive database systems such as Aditi [44], which have used a combination of backward and forward chaining in the classical case, generally compute to the xpoint , i.e. the set of new facts accumulates until no more can be generated. Hence we can think of this as the rules being red in parallel as many times as needed in order to ....

Vaghani, J., K. Ramamohanarao, D. Kemp, Z. Somogyi, P. Stuckey, T. Leask and J. Harland. The Aditi Deductive Database System, VLDB Journal 3:2:245288, April, 1994.

....of the modus ponens rule in intuitionistic logic is that it preserves equivalence: This strong property greatly simplifies the analysis of this rule of inference. A combination of both backward and forward chaining may be found in deductive database systems such as Aditi [20]. In such systems, which are based on variants of Prolog, forward chaining is generally used in order to compute all answers to a query using efficient join algorithms and other techniques from relational databases, whilst backward chaining is used for less data intensive computational tasks (such ....

J. Vaghani, K. Ramamohanarao, D. Kemp, Z. Somogyi, P. Stuckey, T. Leask and J. Harland. The Aditi Deductive Database System. VLDB Journal 3:2:245-288, April, 1994.

....he decides to spend an extra 300 to travel with Deluxe Airways, even though there are two cheaper flights. In this paper we describe our experiences with an implementation of such an application in the Aditi deductive database system, which has been developed at the University of Melbourne [8]. There are several reasons why a flights database seems to be a good choice for a demonstration of deductive database technology. Firstly, recursive rules are needed. In our experience, it seems that deductive databases will not become commercially accepted until they are perceived to be not ....

....interface, a graphical user interface, an interface to the Ingres database system via embedded SQL, and a programming interface to Nu Prolog. It is also possible to embed top down computations within Aditi code. For a more detailed description of the Aditi system, the reader is referred to [8]. 2.2 The Nu Prolog Interface A useful feature of Aditi is that there is a two way interface between Aditi and Nu Prolog, in that a Nu Prolog program can make call to Aditi, and an Aditi program can make calls to Nu Prolog. In this way a Prolog program can be used either as a pre (or post ) ....

J. Vaghani, K. Ramamohanarao, D. Kemp, Z. Somogyi, P. Stuckey, T. Leask and J. Harland, The Aditi Deductive Database System, VLDB Journal 3:2:245-288, April, 1994. 13

....order of the predicates in the recursive rule is important, as due to Prolog s computation rule, if the predicates are in the reverse order, then goals such as path(a,Y) will loop forever. This problem can be avoided by using a memoing system such as XSB [28] or a bottomup system such as Aditi [26]. However, it is common to re write the program above so that the path found is returned as part of the answer. In such cases, system such as XSB and Aditi will only work for graphs which are acyclic. For example, consider the program below. path(X,Y, X,Y] edge(X,Y) path(X,Y, X Path] ....

J. Vaghani, K. Ramamohanarao, D. Kemp, Z. Somogyi, P. Stuckey, T. Leask and J. Harland. The Aditi Deductive Database System. VLDB J. 3:2:245--288, 1994.

....deepening search, the Andorra model, etc. and yet it would seem to be useful to reason about the behaviour of programs in a variety of execution models. For example, consider a program containing the rule p : q, p. In Prolog, the goal p will run forever. In a system such as XSB[12] or Aditi[11], this query will terminate with failure. Hence we may see this as a bad Prolog program (it fails to terminate) and a good XSB or Aditi program (it terminates) However, there may be cases in which the 5 non termination is desirable, such as in a command processor or operating system, in ....

J. Vaghani, K. Ramamohanarao, D. Kemp, Z. Somogyi, P. Stuckey, T. Leask and J. Harland. The Aditi Deductive Database System. VLDB J. 3:2:245--288, 1994.

....of the modus ponens rule in intuitionistic logic is that it preserves equivalence: This strong property greatly simplifies the analysis of this rule of inference. A combination of both backward and forward chaining may be found in deductive database systems such as Aditi [11]. In such systems, which are based on variants of Prolog, forward chaining is generally used in order to compute all answers to a query using efficient join algorithms and other techniques from relational databases, whilst backward chaining is used for less data intensive computational tasks (such ....

J. Vaghani, K. Ramamohanarao, D. Kemp, Z. Somogyi, P. Stuckey, T. Leask and J. Harland. The Aditi Deductive Database System. VLDB Journal 3:2:245-288, April, 1994.

....of the modus ponens rule in intuitionistic logic is that it preserves equivalence: This strong property greatly simplifies the analysis of this rule of inference. A combination of both backward and forward chaining may be found in deductive database systems such as Aditi [20]. In such systems, which are based on variants of Prolog, forward chaining is generally used in order to compute all answers to a query using efficient join algorithms and other techniques from relational databases, whilst backward chaining is used for less data intensive computational tasks (such ....

J. Vaghani, K. Ramamohanarao, D. Kemp, Z. Somogyi, P. Stuckey, T. Leask and J. Harland. The Aditi Deductive Database System. VLDB Journal 3:2:245-288, April, 1994.

....is scanned in parallel, and records with matching join attributes are joined. By first sorting both relations, the merging phase is performed in linear time in the size of the relations. The sort merge algorithm presented below is similar to the version used in the Aditi deductive database system [80], and is more general than that described above. During the sorting phase, the relations are not fully sorted. Instead, each relation is divided into partitions which are sorted. The size of the partitions is the size of the memory buffer, B. During the merging phase, the sorted partitions of each ....

....16 milliseconds, whereas reading two random blocks will take 28 milliseconds. When the cost of a join is calculated, the difference in this time should be taken into account. The CPU cost of a join should also be taken into account. Experience with the Aditi deductive database, by Vaghani et al. [80], showed that the disk access and transfer times amount to between 10 and 20 of the time taken to perform a join. Thus, the CPU time is an important factor which should be considered when determining the most efficient method to perform any given join. In this chapter, we describe a more ....

J. Vaghani, K. Ramamohanarao, D. B. Kemp, Z. Somogyi, P. J. Stuckey, T. S. Leask, and J. Harland. The Aditi deductive database system. The VLDB Journal, 3(2):245--288, 1994. 192

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J. Vaghani, K. Ramamohanarao, D. Kemp, Z. Somogyi, P. Stuckey, T. Leask and J. Harland. The Aditi Deductive Database System. VLDB J. 3:2:245#288, 1994.

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Jayen Vaghani, Kotagiri Ramamohanarao, David B. Kemp, Zoltan Somogyi, Peter J. Stuckey, Tim S. Leask, and James Harland. The Aditi deductive database system. VLDB Journal, 3:245--288, 1994.

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J. Vaghani, K. Ramamohanarao, D. B. Kemp, Z. Somogyi, P. J. Stuckey, T. S. Leask, and J. Harland. The Aditi deductive database system. VLDB Journal: Very Large Data Bases, 3(2):245-288, 1994.

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Jayen Vaghani, Kotagiri Ramamohanarao, David B. Kemp, Zoltan Somogyi, and Peter J. Stuckey. The Aditi Deductive Database System. In Jan Chomicki, editor, Proceedings of the NACLP'90 Workshop on Deductive Databases. Kansas State University Technical Report TR-CS-90-14, 1990.

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J. Vaghani, K. Ramamohanarao, D. B. Kemp, Z. Somogyi, P. J. Stuckey, T. S. Leask, and J. Harland. The Aditi deductive database system. The VLDB journal, 3(2):245-288, 1994.

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J. Vaghani, K. Ramamohanarao, D.B. Kemp, Z. Somogyi, P.J. Stuckey, T.S. Leask, and J. Harland. The Aditi deductive database system. The VLDB Journal, 3(2):245--288, 1994.

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