Journal articles: 'Datamol'

1

Eiter, Thomas, Georg Gottlob, and Heikki Mannila. "Disjunctive datalog." ACM Transactions on Database Systems 22, no. 3 (September 1997): 364–418. http://dx.doi.org/10.1145/261124.261126.

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Gottlob, Georg, Erich Grädel, and Helmut Veith. "Datalog LITE." ACM Transactions on Computational Logic 3, no. 1 (January 2002): 42–79. http://dx.doi.org/10.1145/504077.504079.

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3

Grau, Bernardo Cuenca, Ian Horrocks, Mark Kaminski, Egor V. Kostylev, and Boris Motik. "Limit Datalog." ACM SIGMOD Record 48, no. 4 (February 25, 2020): 6–17. http://dx.doi.org/10.1145/3385658.3385660.

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4

Deagustini, Cristhian Ariel D., Maria Vanina Martinez, Marcelo A. Falappa, and Guillermo R. Simari. "Datalog+- Ontology Consolidation." Journal of Artificial Intelligence Research 56 (August 30, 2016): 613–56. http://dx.doi.org/10.1613/jair.5131.

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Knowledge bases in the form of ontologies are receiving increasing attention as they allow to clearly represent both the available knowledge, which includes the knowledge in itself and the constraints imposed to it by the domain or the users. In particular, Datalog± ontologies are attractive because of their property of decidability and the possibility of dealing with the massive amounts of data in real world environments; however, as it is the case with many other ontological languages, their application in collaborative environments often lead to inconsistency related issues. In this paper we introduce the notion of incoherence regarding Datalog± ontologies, in terms of satisfiability of sets of constraints, and show how under specific conditions incoherence leads to inconsistent Datalog± ontologies. The main contribution of this work is a novel approach to restore both consistency and coherence in Datalog± ontologies. The proposed approach is based on kernel contraction and restoration is performed by the application of incision functions that select formulas to delete. Nevertheless, instead of working over minimal incoherent/inconsistent sets encountered in the ontologies, our operators produce incisions over non-minimal structures called clusters. We present a construction for consolidation operators, along with the properties expected to be satisfied by them. Finally, we establish the relation between the construction and the properties by means of a representation theorem. Although this proposal is presented for Datalog± ontologies consolidation, these operators can be applied to other types of ontological languages, such as Description Logics, making them apt to be used in collaborative environments like the Semantic Web.

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5

Meskes, Michael. "Subsumption-stratified datalog." Journal of Logic Programming 36, no. 1 (July 1998): 55–90. http://dx.doi.org/10.1016/s0743-1066(97)10007-3.

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6

Stüber, Torsten, and Heiko Vogler. "Weighted monadic datalog." Theoretical Computer Science 403, no. 2-3 (August 2008): 221–38. http://dx.doi.org/10.1016/j.tcs.2008.04.025.

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7

Ben-Shlomo, Y., J. Head, and A. J. Lees. "Mortality in DATATOP." Annals of Neurology 45, no. 1 (January 1999): 138–39. http://dx.doi.org/10.1002/1531-8249(199901)45:1<138::aid-art27>3.0.co;2-9.

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8

ALVIANO, MARIO, WOLFGANG FABER, NICOLA LEONE, and MARCO MANNA. "Disjunctive datalog with existential quantifiers: Semantics, decidability, and complexity issues." Theory and Practice of Logic Programming 12, no. 4-5 (July 2012): 701–18. http://dx.doi.org/10.1017/s1471068412000257.

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AbstractDatalog is one of the best-known rule-based languages, and extensions of it are used in a wide context of applications. An important Datalog extension is Disjunctive Datalog, which significantly increases the expressivity of the basic language. Disjunctive Datalog is useful in a wide range of applications, ranging from Databases (e.g., Data Integration) to Artificial Intelligence (e.g., diagnosis and planning under incomplete knowledge). However, in recent years an important shortcoming of Datalog-based languages became evident, e.g. in the context of data-integration (consistent query-answering, ontology-based data access) and Semantic Web applications: The language does not permit any generation of and reasoning with unnamed individuals in an obvious way. In general, it is weak in supporting many cases of existential quantification. To overcome this problem, Datalog∃ has recently been proposed, which extends traditional Datalog by existential quantification in rule heads. In this work, we propose a natural extension of Disjunctive Datalog and Datalog∃, called Datalog∃,˅, which allows both disjunctions and existential quantification in rule heads and is therefore an attractive language for knowledge representation and reasoning, especially in domains where ontology-based reasoning is needed. We formally define syntax and semantics of the language Datalog∃,˅, and provide a notion of instantiation, which we prove to be adequate for Datalog∃,˅. A main issue of Datalog∃ and hence also of Datalog∃,˅ is that decidability is no longer guaranteed for typical reasoning tasks. In order to address this issue, we identify many decidable fragments of the language, which extend, in a natural way, analog classes defined in the non-disjunctive case. Moreover, we carry out an in-depth complexity analysis, deriving interesting results which range from Logarithmic Space to Exponential Time.

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9

Kaminski, Mark, Yavor Nenov, and Bernardo Cuenca Grau. "Datalog rewritability of Disjunctive Datalog programs and non-Horn ontologies." Artificial Intelligence 236 (July 2016): 90–118. http://dx.doi.org/10.1016/j.artint.2016.03.006.

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10

NOTTELMANN, HENRIK, and NORBERT FUHR. "ADDING PROBABILITIES AND RULES TO OWL LITE SUBSETS BASED ON PROBABILISTIC DATALOG." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14, no. 01 (February 2006): 17–41. http://dx.doi.org/10.1142/s0218488506003819.

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This paper proposes two probabilistic extensions of variants of the OWL Lite description language, which are essential for advanced applications like information retrieval. The first step follows the axiomatic approach of combining description logics and Horn clauses: Subsets of OWL Lite are mapped in a sound and complete way onto Horn predicate logics (Datalog variants). Compared to earlier approaches, a larger fraction of OWL Lite can be transformed by switching to Datalog with equality in the head; however, some OWL Lite constructs cannot be transformed completely into Datalog. By using probabilistic Datalog, the new probabilistic OWL Lite subsets (both with support for Horn rules) are defined, and the semantics are given by the semantics of the corresponding probabilistic Datalog program. As inference engines for probabilistic Datalog are available, description logics and information retrieval systems can easily be combined.

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11

CHARALAMBIDIS, ANGELOS, CHRISTOS NOMIKOS, and PANOS RONDOGIANNIS. "The Expressive Power of Higher-Order Datalog." Theory and Practice of Logic Programming 19, no. 5-6 (September 2019): 925–40. http://dx.doi.org/10.1017/s1471068419000279.

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AbstractA classical result in descriptive complexity theory states that Datalog expresses exactly the class of polynomially computable queries on ordered databases (Papadimitriou 1985; Grädel 1992; Vardi 1982; Immerman 1986; Leivant 1989). In this paper we extend this result to the case of higher-order Datalog. In particular, we demonstrate that on ordered databases, for all k ≥ 2, k-order Datalog captures (k − 1)-EXPTIME. This result suggests that higher-order extensions of Datalog possess superior expressive power and they are worthwhile of further investigation both in theory and in practice.

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12

Afrati, Foto, Manolis Gergatsoulis, and Francesca Toni. "Linearisability on datalog programs." Theoretical Computer Science 308, no. 1-3 (November 2003): 199–226. http://dx.doi.org/10.1016/s0304-3975(02)00730-2.

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13

Ketsman, Bas, Aws Albarghouthi, and Paraschos Koutris. "Distribution Policies for Datalog." Theory of Computing Systems 64, no. 5 (December 4, 2019): 965–98. http://dx.doi.org/10.1007/s00224-019-09959-3.

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14

Daroff, R. B. "Neuromythology IX and DATATOP." Neurology 41, no. 10 (October 1, 1991): 1703. http://dx.doi.org/10.1212/wnl.41.10.1703.

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15

Lechtenberg, R. "Neuromythology IX and DATATOP." Neurology 41, no. 10 (October 1, 1991): 1703. http://dx.doi.org/10.1212/wnl.41.10.1703-a.

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16

Smith, M. S. "Neuromythology IX and DATATOP." Neurology 41, no. 10 (October 1, 1991): 1703. http://dx.doi.org/10.1212/wnl.41.10.1703-b.

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17

Muther, E. F. "Neuromythology IX and DATATOP." Neurology 41, no. 10 (October 1, 1991): 1703. http://dx.doi.org/10.1212/wnl.41.10.1703-c.

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18

MD, M. M. "Neuromythology IX and DATATOP." Neurology 41, no. 10 (October 1, 1991): 1703. http://dx.doi.org/10.1212/wnl.41.10.1703-d.

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19

Plum, F. "Neuromythology IX and DATATOP." Neurology 41, no. 10 (October 1, 1991): 1703. http://dx.doi.org/10.1212/wnl.41.10.1703-e.

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20

Hurtig, H. I. "Neuromythology IX and DATATOP." Neurology 41, no. 10 (October 1, 1991): 1704. http://dx.doi.org/10.1212/wnl.41.10.1704.

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21

Lossing, J. H. "Neuromythology IX and DATATOP." Neurology 41, no. 10 (October 1, 1991): 1704. http://dx.doi.org/10.1212/wnl.41.10.1704-a.

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22

GRECO, SERGIO, and CARLO ZANIOLO. "Greedy algorithms in Datalog." Theory and Practice of Logic Programming 1, no. 4 (June 25, 2001): 381–407. http://dx.doi.org/10.1017/s1471068401001090.

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In the design of algorithms, the greedy paradigm provides a powerful tool for solving efficiently classical computational problems, within the framework of procedural languages. However, expressing these algorithms within the declarative framework of logic-based languages has proven a difficult research challenge. In this paper, we extend the framework of Datalog-like languages to obtain simple declarative formulations for such problems, and propose effective implementation techniques to ensure computational complexities comparable to those of procedural formulations. These advances are achieved through the use of the choice construct, extended with preference annotations to effect the selection of alternative stable-models and nondeterministic fixpoints. We show that, with suitable storage structures, the differential fixpoint computation of our programs matches the complexity of procedural algorithms in classical search and optimization problems.

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23

Greco, Sergio, and Cristian Molinaro. "Datalog and Logic Databases." Synthesis Lectures on Data Management 7, no. 2 (November 5, 2015): 1–169. http://dx.doi.org/10.2200/s00648ed1v01y201505dtm041.

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24

Arntzenius, Michael, and Neelakantan R. Krishnaswami. "Datafun: a functional Datalog." ACM SIGPLAN Notices 51, no. 9 (December 5, 2016): 214–27. http://dx.doi.org/10.1145/3022670.2951948.

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25

Bimberg, Dieter. "Ultrafast VCSELs for Datacom." IEEE Photonics Journal 2, no. 2 (April 2010): 273–75. http://dx.doi.org/10.1109/jphot.2010.2047386.

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26

Zhao, David, Pavle Subotić, and Bernhard Scholz. "Debugging Large-scale Datalog." ACM Transactions on Programming Languages and Systems 42, no. 2 (May 27, 2020): 1–35. http://dx.doi.org/10.1145/3379446.

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27

Basta, Stefano, Sergio Flesca, and Sergio Greco. "Functional queries in Datalog." New Generation Computing 20, no. 4 (December 2002): 339–71. http://dx.doi.org/10.1007/bf03037371.

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28

Greco, Sergio, Luigi Palopoli, and Eugenio Spadafora. "Extending Datalog with arrays." Data & Knowledge Engineering 17, no. 1 (October 1995): 31–57. http://dx.doi.org/10.1016/0169-023x(95)00027-p.

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29

Chaudhuri, Surajit, and Phokion G. Kolaitis. "Can Datalog Be Approximated?" Journal of Computer and System Sciences 55, no. 2 (October 1997): 355–69. http://dx.doi.org/10.1006/jcss.1997.1528.

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30

Bonner, Anthony, and Giansalvatore Mecca. "Sequences, Datalog, and Transducers." Journal of Computer and System Sciences 57, no. 3 (December 1998): 234–59. http://dx.doi.org/10.1006/jcss.1998.1562.

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31

Meyden, Ron van der. "PREDICATE BOUNDEDNESS OF LINEAR MONADIC DATALOG IS IN PSPACE." International Journal of Foundations of Computer Science 11, no. 04 (December 2000): 591–612. http://dx.doi.org/10.1142/s0129054100000351.

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Datalog programs, are a special case of logic programs without function symbols. Detection of boundedness permits Datalog programs to be optimized by the elimination of recursion. To determine whether a predicate is bounded in a Datalog program is known to be undecidable. However, previous work (Cosmadakis et al., 20th ACM Symposium on the Theory of Computing, 1988) has show that for monadic Datalog, this problem is decidable, with upper and lower bounds of EXPSPACE and PSPACE respectively for linear monadic programs. We establish that predicate boundedness for linear monadic programs is in fact in PSPACE, yielding a tight bound for this problem.a

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32

Pal, Singht. "Oracle acquired AI startup DataFox." Industrija 47, no. 2 (2019): 77–95. http://dx.doi.org/10.5937/industrija47-20182.

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33

Ichsan, Muhammad, and Fitria Fitria. "Information System Web-based Computer Service Case Study Datacom." Phasti: Jurnal Teknik Informatika Politeknik Hasnur 6, no. 01 (March 26, 2021): 13–20. http://dx.doi.org/10.46365/pha.v5i01.413.

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Datacom is a private company engaged in the sale and repair of computers. Services in the datacom must be done properly and correctly to facilitate and speed up the flow of services, a computer data processing information system is very appropriate to simplify services on the datacom. This study aims to produce a computer service information system in Datacom, this system is built based on a website using the Codeigniter Framework and will be developed using the Waterfall method. Testing on this system using Blackbox Testing results in the system being able to check the status of computer service, can print service data, and can print data service reports per month.

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34

Green, Todd J. "Datalog and Recursive Query Processing." Foundations and Trends® in Databases 5, no. 2 (2012): 105–95. http://dx.doi.org/10.1561/1900000017.

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35

Hu, Pan, Boris Motik, and Ian Horrocks. "Modular Materialisation of Datalog Programs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2859–66. http://dx.doi.org/10.1609/aaai.v33i01.33012859.

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The seminaïve algorithm can be used to materialise all consequences of a datalog program, and it also forms the basis for algorithms that incrementally update a materialisation as the input facts change. Certain (combinations of) rules, however, can be handled much more efficiently using custom algorithms. To integrate such algorithms into a general reasoning approach that can handle arbitrary rules, we propose a modular framework for computing and maintaining a materialisation. We split a datalog program into modules that can be handled using specialised algorithms, and we handle the remaining rules using the semina¨ıve algorithm. We also present two algorithms for computing the transitive and the symmetric– transitive closure of a relation that can be used within our framework. Finally, we show empirically that our framework can handle arbitrary datalog programs while outperforming existing approaches, often by orders of magnitude.

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36

Sankpal, Prof S. S. "Portable Device Monitoring and Datalog." International Journal for Research in Applied Science and Engineering Technology 7, no. 4 (April 30, 2019): 1725–28. http://dx.doi.org/10.22214/ijraset.2019.4313.

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37

Achs, Ágnes. "Fuzzy Datalog with background knowledge." Teaching Mathematics and Computer Science 3, no. 2 (2005): 257–81. http://dx.doi.org/10.5485/tmcs.2005.0098.

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38

Ajtai, Miklos, and Yuri Gurevich. "Datalog vs first-order logic." Journal of Computer and System Sciences 49, no. 3 (December 1994): 562–88. http://dx.doi.org/10.1016/s0022-0000(05)80071-6.

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39

Guessarian, Irène, Eugénie Foustoucos, Theodore Andronikos, and Foto Afrati. "On temporal logic versus datalog." Theoretical Computer Science 303, no. 1 (June 2003): 103–33. http://dx.doi.org/10.1016/s0304-3975(02)00447-4.

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40

Flum, Jörg, Max Kubierschky, and Bertram Ludäscher. "Games and total Datalog¬ queries." Theoretical Computer Science 239, no. 2 (May 2000): 257–76. http://dx.doi.org/10.1016/s0304-3975(99)00222-4.

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41

Sáenz-Pérez, Fernando. "Restricted Predicates for Hypothetical Datalog." Electronic Proceedings in Theoretical Computer Science 200 (December 19, 2015): 64–79. http://dx.doi.org/10.4204/eptcs.200.5.

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42

Toman, David, and Jan Chomicki. "Datalog with integer periodicity constraints." Journal of Logic Programming 35, no. 3 (June 1998): 263–90. http://dx.doi.org/10.1016/s0743-1066(97)10008-5.

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43

Ben-Eliyahu-Zohary, Rachel, Luigi Palopoli, and Victoria Zemlyanker. "More on tractable disjunctive Datalog." Journal of Logic Programming 46, no. 1-2 (November 2000): 61–101. http://dx.doi.org/10.1016/s0743-1066(99)00066-7.

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44

Halevy, Alon Y., Inderpal Singh Mumick, Yehoshua Sagiv, and Oded Shmueli. "Static analysis in datalog extensions." Journal of the ACM 48, no. 5 (September 2001): 971–1012. http://dx.doi.org/10.1145/502102.502104.

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45

BáRány, Vince, Balder Ten Cate, Benny Kimelfeld, Dan Olteanu, and Zografoula Vagena. "Declarative Probabilistic Programming with Datalog." ACM Transactions on Database Systems 42, no. 4 (November 13, 2017): 1–35. http://dx.doi.org/10.1145/3132700.

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46

Alves, M. "Update rules in datalog programs." Journal of Logic and Computation 8, no. 6 (December 1, 1998): 745–75. http://dx.doi.org/10.1093/logcom/8.6.745.

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47

Stevens, Orrin. "The History of Datacom/DB." IEEE Annals of the History of Computing 31, no. 4 (October 2009): 87–91. http://dx.doi.org/10.1109/mahc.2009.108.

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48

Ameloot, Tom J., Bas Ketsman, Frank Neven, and Daniel Zinn. "Datalog Queries Distributing over Components." ACM Transactions on Computational Logic 18, no. 1 (April 13, 2017): 1–35. http://dx.doi.org/10.1145/3022743.

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49

Bonner, Anthony J. "Hypothetical datalog: complexity and expressibility." Theoretical Computer Science 76, no. 1 (October 1990): 3–51. http://dx.doi.org/10.1016/0304-3975(90)90011-6.

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50

Abiteboul, Serge, and Richard Hull. "Data functions, datalog and negation." ACM SIGMOD Record 17, no. 3 (June 1988): 143–53. http://dx.doi.org/10.1145/971701.50218.

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Journal articles on the topic 'Datamol'

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