MODELING DYNAMICS OF DATABASES WITH RELATIONAL DISCRETE EVENT SYSTEMS AND MODELS

Behavior of relational databases is studied within the framework of Relational Discrete Event Systems (RDESes) and Models (RDEMs). Three behavior specification methods based on production systems, recurrence equations, and Petri nets are defined and their expressive powers are compared. Production system RDEM is extended to support non-determinism, and various deterministic and non-deterministic production system interpreters are introduced and formally compared in terms of their expressive power. It is shown that the parallel deterministic interpreter has more expressive power than other interpreters including an OPS5-like interpreter. Since it is also parallel, this makes the parallel deterministic interpreter a very attractive interpreter for production systems.Information Systems Working Papers Serie

On specifying database updates

AbstractWe address the problem of formalizing the evolution of a database under the effect of an arbitrary sequence of update transactions. We do so by appealing to a first-order representation language called the situation calculus, which is a standard approach in artificial intelligence to the formalization of planning problems. We formalize database transactions in exactly the same way as actions in the artificial intelligence planning domain. This leads to a database version of the frame problem in artificial intelligence. We provide a solution to the frame problem for a special, but substantial, class of update transactions. Using the axioms corresponding to this solution, we provide procedures for determining whether a given sequence of update transactions is legal, and for query evaluation in an updated database. These procedures have the desirable property that they appeal to theorem-proving only with respect to the initial database state.We next address the problem of proving properties true in all states of the database. It turns out that mathematical induction is required for this task, and we formulate a number of suitable induction principles. Among those properties of database states that we wish to prove are the standard database notions of static and dynamic integrity constraints. In our setting, these emerge as inductive entailments of the database.Finally, we discuss various possible extensions of the approach of this paper, including transaction logs and historical queries, the complexity of query evaluation, actualized transactions, logic programming approaches to updates, database views, and state constraints

Measure-Based Inconsistency-Tolerant Maintenance of Database Integrity

[EN] To maintain integrity, constraint violations should be prevented or repaired. However, it may not be feasible to avoid inconsistency, or to repair all violations at once. Based on an abstract concept of violation measures, updates and repairs can be checked for keeping inconsistency bounded, such that integrity violations are guaranteed to never get out of control. This measure-based approach goes beyond conventional methods that are not meant to be applied in the presence of inconsistency. It also generalizes recently introduced concepts of inconsistency-tolerant integrity maintenance.Partially supported by FEDER and the Spanish grants TIN2009-14460-C03 and TIN2010-17139Decker, H. (2013). Measure-Based Inconsistency-Tolerant Maintenance of Database Integrity. Lecture Notes in Computer Science. 7693:149-173. https://doi.org/10.1007/978-3-642-36008-4_7S1491737693Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison-Wesley (1995)Abiteboul, S., Vianu, V.: A transaction-based approach to relational database specification. JACM 36(4), 758–789 (1989)Afrati, F., Kolaitis, P.: Repair checking in inconsistent databases: algorithms and complexity. In: 12th ICDT, pp. 31–41. ACM Press (2009)Arenas, M., Bertossi, L., Chomicki, J.: Consistent query answers in inconsistent databases. In: PODS 1999, pp. 68–79. ACM Press (1999)Arieli, O., Denecker, M., Bruynooghe, M.: Distance semantics for database repair. Ann. Math. Artif. Intell. 50, 389–415 (2007)Arni-Bloch, N., Ralyté, J., Léonard, M.: Service–Driven Information Systems Evolution: Handling Integrity Constraints Consistency. In: Persson, A., Stirna, J. (eds.) PoEM 2009. LNBIP, vol. 39, pp. 191–206. Springer, Heidelberg (2009)Bauer, H.: Maß- und Integrationstheorie, 2. Auflage. De Gruyter (1992)Besnard, P., Hunter, A.: Quasi-Classical Logic: Non-Trivializable Classical Reasoning from Inconsistent Information. In: Froidevaux, C., Kohlas, J. (eds.) ECSQARU 1995. LNCS, vol. 946, pp. 44–51. Springer, Heidelberg (1995)Bohanon, P., Fan, W., Flaster, M., Rastogi, R.: A Cost-Based Model and Effective Heuristic for Repairing Constraints by Value Modification. In: Proc. SIGMOD 2005, pp. 143–154. ACM Press (2005)Ceri, S., Cochrane, R., Widom, J.: Practical Applications of Triggers and Constraints: Success and Lingering Issues. In: Proc. 26th VLDB, pp. 254–262. Morgan Kaufmann (2000)Chakravarthy, U., Grant, J., Minker, J.: Logic-based Approach to Semantic Query Optimization. Transactions on Database Systems 15(2), 162–207 (1990)Chomicki, J.: Consistent Query Answering: Five Easy Pieces. In: Schwentick, T., Suciu, D. (eds.) ICDT 2007. LNCS, vol. 4353, pp. 1–17. Springer, Heidelberg (2006)Christiansen, H., Martinenghi, D.: On simplification of database integrity constraints. Fundamenta Informaticae 71(4), 371–417 (2006)Clark, K.: Negation as Failure. In: Gallaire, H., Minker, J. (eds.) Logic and Data Bases, pp. 293–322. Plenum Press (1978)Curino, C., Moon, H., Deutsch, A., Zaniolo, C.: Update Rewriting and Integrity Constraint Maintenance in a Schema Evolution Support System: PRISM++. PVLDB 4, 117–128 (2010)Dawson, J.: The compactness of first-order logic: From Gödel to Lindström. History and Philosophy of Logic 14(1), 15–37 (1993)Decker, H.: The Range Form of Databases and Queries or: How to Avoid Floundering. In: Proc. 5th ÖGAI. Informatik-Fachberichte, vol. 208, pp. 114–123. Springer (1989)Decker, H.: Drawing Updates From Derivations. In: Kanellakis, P.C., Abiteboul, S. (eds.) ICDT 1990. LNCS, vol. 470, pp. 437–451. Springer, Heidelberg (1990)Decker, H.: Extending Inconsistency-Tolerant Integrity Checking by Semantic Query Optimization. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds.) DEXA 2008. LNCS, vol. 5181, pp. 89–96. Springer, Heidelberg (2008)Decker, H.: Answers That Have Integrity. In: Schewe, K.-D., Thalheim, B. (eds.) SDKB 2010. LNCS, vol. 6834, pp. 54–72. Springer, Heidelberg (2011)Decker, H.: Causes of the Violation of Integrity Constraints for Supporting the Quality of Databases. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011, Part V. LNCS, vol. 6786, pp. 283–292. Springer, Heidelberg (2011)Decker, H.: Inconsistency-tolerant Integrity Checking based on Inconsistency Metrics. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part II. LNCS, vol. 6882, pp. 548–558. Springer, Heidelberg (2011)Decker, H.: Partial Repairs that Tolerate Inconsistency. In: Eder, J., Bielikova, M., Tjoa, A.M. (eds.) ADBIS 2011. LNCS, vol. 6909, pp. 389–400. Springer, Heidelberg (2011)Decker, H.: Consistent Explanations of Answers to Queries in Inconsistent Knowledge Bases. In: Roth-Berghofer, T., Tintarev, N., Leake, D. (eds.) Explanation-aware Computing, Proc. IJCAI 2011 Workshop ExaCt 2011, pp. 71–80 (2011), http://exact2011.workshop.hm/index.phpDecker, H., Martinenghi, D.: Classifying integrity checking methods with regard to inconsistency tolerance. In: Proc. PPDP 2008, pp. 195–204. ACM Press (2008)Decker, H., Martinenghi, D.: Modeling, Measuring and Monitoring the Quality of Information. In: Heuser, C.A., Pernul, G. (eds.) ER 2009. LNCS, vol. 5833, pp. 212–221. Springer, Heidelberg (2009)Decker, H., Martinenghi, D.: Inconsistency-tolerant Integrity Checking. IEEE TKDE 23(2), 218–234 (2011)Decker, H., Muñoz-Escoí, F.D.: Revisiting and Improving a Result on Integrity Preservation by Concurrent Transactions. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM 2010 Workshops. LNCS, vol. 6428, pp. 297–306. Springer, Heidelberg (2010)Dung, P., Kowalski, R., Toni, F.: Dialectic Proof Procedures for Assumption-based Admissible Argumentation. Artificial Intelligence 170(2), 114–159 (2006)Ebbinghaus, H.-D., Flum, J.: Finite Model Theory, 2nd edn. Springer (2006)Embury, S., Brandt, S., Robinson, J., Sutherland, I., Bisby, F., Gray, A., Jones, A., White, R.: Adapting integrity enforcement techniques for data reconciliation. Information Systems 26, 657–689 (2001)Enderton, H.: A Mathematical Introduction to Logic, 2nd edn. Academic Press (2001)Eiter, T., Fink, M., Greco, G., Lembo, D.: Repair localization for query answering from inconsistent databases. ACM TODS 33(2), article 10 (2008)Furfaro, F., Greco, S., Molinaro, C.: A three-valued semantics for querying and repairing inconsistent databases. Ann. Math. Artif. Intell. 51(2-4), 167–193 (2007)Grant, J., Hunter, A.: Measuring the Good and the Bad in Inconsistent Information. In: Proc. 22nd IJCAI, pp. 2632–2637 (2011)Greco, G., Greco, S., Zumpano, E.: A logical framework for querying and repairing inconsistent databases. IEEE TKDE 15(6), 1389–1408 (2003)Guessoum, A., Lloyd, J.: Updating knowledge bases. New Generation Computing 8(1), 71–89 (1990)Guessoum, A., Lloyd, J.: Updating knowledge bases II. New Generation Computing 10(1), 73–100 (1991)Gupta, A., Sagiv, Y., Ullman, J., Widom, J.: Constraint checking with partial information. In: Proc. PODS 1994, pp. 45–55. ACM Press (1994)Hunter, A.: Measuring Inconsistency in Knowledge via Quasi-Classical Models. In: Proc. 18th AAAI &14th IAAI, pp. 68–73 (2002)Hunter, A., Konieczny, S.: Approaches to Measuring Inconsistent Information. In: Bertossi, L., Hunter, A., Schaub, T. (eds.) Inconsistency Tolerance. LNCS, vol. 3300, pp. 191–236. Springer, Heidelberg (2005)Hunter, A., Konieczny, S.: Measuring inconsistency through minimal inconsistent sets. In: Brewka, G., Lang, J. (eds.) Principles of Knowledge Representation and Reasoning (Proc. 11th KR), pp. 358–366. AAAI Press (2008)Hunter, A., Konieczny, S.: On the measure of conflicts: Shapley Inconsistency Values. Artificial Intelligence 174, 1007–1026 (2010)Kakas, A., Mancarella, P.: Database updates through abduction. In: Proc. 16th VLDB, pp. 650–661. Morgan Kaufmann (1990)Kakas, A., Kowalski, R., Toni, F.: The role of Abduction in Logic Programming. In: Gabbay, D., Hogger, C., Robinson, J.A. (eds.) Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 5, pp. 235–324. Oxford University Press (1998)Lee, S.Y., Ling, T.W.: Further improvements on integrity constraint checking for stratifiable deductive databases. In: Proc. VLDB 1996, pp. 495–505. Morgan Kaufmann (1996)Lehrer, K.: Relevant Deduction and Minimally Inconsistent Sets. Journal of Philosophy 3(2,3), 153–165 (1973)Mu, K., Liu, W., Jin, Z., Bell, D.: A Syntax-based Approach to Measuring the Degree of Inconsistency for Belief Bases. J. Approx. Reasoning 52(7), 978–999 (2011)Lloyd, J., Sonenberg, L., Topor, R.: Integrity constraint checking in stratified databases. J. Logic Programming 4(4), 331–343 (1987)Lozinskii, E.: Resolving contradictions: A plausible semantics for inconsistent systems. J. Automated Reasoning 12(1), 1–31 (1994)Ma, Y., Qi, G., Hitzler, P.: Computing inconsistency measure based on paraconsistent semantics. J. Logic Computation 21(6), 1257–1281 (2011)Martinenghi, D., Christiansen, H.: Transaction Management with Integrity Checking. In: Andersen, K.V., Debenham, J., Wagner, R. (eds.) DEXA 2005. LNCS, vol. 3588, pp. 606–615. Springer, Heidelberg (2005)Martinenghi, D., Christiansen, H., Decker, H.: Integrity Checking and Maintenance in Relational and Deductive Databases and Beyond. In: Ma, Z. (ed.) Intelligent Databases: Technologies and Applications, pp. 238–285. IGI Global (2006)Martinez, M.V., Pugliese, A., Simari, G.I., Subrahmanian, V.S., Prade, H.: How Dirty Is Your Relational Database? An Axiomatic Approach. In: Mellouli, K. (ed.) ECSQARU 2007. LNCS (LNAI), vol. 4724, pp. 103–114. Springer, Heidelberg (2007)Meyer, J., Wieringa, R. (eds.): Deontic Logic in Computer Science. Wiley (1994)Nicolas, J.M.: Logic for improving integrity checking in relational data bases. Acta Informatica 18, 227–253 (1982)Plexousakis, D., Mylopoulos, J.: Accommodating Integrity Constraints During Database Design. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 495–513. Springer, Heidelberg (1996)Rahm, E., Do, H.: Data Cleaning: Problems and Current Approaches. Data Engineering Bulletin 23(4), 3–13 (2000)Sadri, F., Kowalski, R.: A theorem-proving approach to database integrity. In: Minker, J. (ed.) Foundations of Deductive Databases and Logic Programming, pp. 313–362. Morgan Kaufmann (1988)Thimm, M.: Measuring Inconsistency in Probabilistic Knowledge Bases. In: Proc. 25th UAI, pp. 530–537. AUAI Press (2009)Vardi, M.: On the integrity of databases with incomplete information. In: Proc. 5th PODS, pp. 252–266. ACM Press (1986)Wijsen, J.: Database repairing using updates. ACM Trans. Database Syst. 30(3), 722–768 (2005

On the Formal Specification and Derivation of Relational Database Applications

The development of database applications is usually carried out informally. The derivation of database programs directly from formal specifications is a well known and unsolved problem. Most of the previous work in the area either tried to solve the problem too generally or was restricted to some trivial aspects, for example deriving the database structure and/or simple operations. This thesis describes an extension to the traditional database design process aimed at formalizing the development of (relational) database applications. Specifically, it gives a complete description of a general method for the specification of relational database applications using Z, as well as a comprehensive description of a set of rules on how to derive database programs from specifications which result from using the method. The method prescribes how to specify all the important aspects of relational database applications, which includes the definition of relations, the specification of constraints, and querying and updating of relations, including error handling. It also addresses more advanced features such as transactions, sorting of results, aggregate functions, etc. However difficult in general, deriving relational database applications directly from Z specifications written according to the method is not arduous. With appropriate tool support, writing formal specifications according to the method and deriving the corresponding relational database programs can be straightforward. Moreover, it should produce code which is standardized and thus easier to understand and maintain. An intrinsic part of the thesis is a prototype which was built to support the method. It provides a syntactic editor for the method and partially implements the mapping for a specific Relational Database Management System (RDBMS), namely the DBPL system