313 research outputs found
Coherent Integration of Databases by Abductive Logic Programming
We introduce an abductive method for a coherent integration of independent
data-sources. The idea is to compute a list of data-facts that should be
inserted to the amalgamated database or retracted from it in order to restore
its consistency. This method is implemented by an abductive solver, called
Asystem, that applies SLDNFA-resolution on a meta-theory that relates
different, possibly contradicting, input databases. We also give a pure
model-theoretic analysis of the possible ways to `recover' consistent data from
an inconsistent database in terms of those models of the database that exhibit
as minimal inconsistent information as reasonably possible. This allows us to
characterize the `recovered databases' in terms of the `preferred' (i.e., most
consistent) models of the theory. The outcome is an abductive-based application
that is sound and complete with respect to a corresponding model-based,
preferential semantics, and -- to the best of our knowledge -- is more
expressive (thus more general) than any other implementation of coherent
integration of databases
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. 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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 Ă–GAI. 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., KĂĽng, 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-EscoĂ, 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. 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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. 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Inconsistency-Tolerant Integrity Checking
All methods for efficient integrity checking require all integrity constraints to be totally satisfied, before any update is executed. However, a certain amount of inconsistency is the rule, rather than the exception in databases. In this paper, we close the gap between theory and practice of integrity checking, i.e., between the unrealistic theoretical requirement of total integrity and the practical need for inconsistency tolerance, which we define for integrity checking methods. We show that most of them can still be used to check whether updates preserve integrity, even if the current state is inconsistent. Inconsistency-tolerant integrity checking proves beneficial both for integrity preservation and query answering. Also, we show that it is useful for view updating, repairs, schema evolution, and other applications.Hendrik Decker has been supported by FEDER and the Spanish MEC grant TIN2006-14738-C02-01. Davide Martinenghi has been supported by the Search Computing (SeCo) project, funded by ERC under the 2008 Call for "IDEAS Advanced Grants." The authors also wish to thank Davide Barbieri for his valuable contribution to the experimental evaluation.Decker, H.; Martinenghi, D. (2011). Inconsistency-Tolerant Integrity Checking. IEEE Transactions on Knowledge and Data Engineering. 23(2):218-234. https://doi.org/10.1109/TKDE.2010.87S21823423
Matching Dependencies with Arbitrary Attribute Values: Semantics, Query Answering and Integrity Constraints
Matching dependencies (MDs) were introduced to specify the identification or
matching of certain attribute values in pairs of database tuples when some
similarity conditions are satisfied. Their enforcement can be seen as a natural
generalization of entity resolution. In what we call the "pure case" of MDs,
any value from the underlying data domain can be used for the value in common
that does the matching. We investigate the semantics and properties of data
cleaning through the enforcement of matching dependencies for the pure case. We
characterize the intended clean instances and also the "clean answers" to
queries as those that are invariant under the cleaning process. The complexity
of computing clean instances and clean answers to queries is investigated.
Tractable and intractable cases depending on the MDs and queries are
identified. Finally, we establish connections with database "repairs" under
integrity constraints.Comment: 13 pages, double column, 2 figure
Database repairs with answer set programming
Dissertação para obtenção do Grau de Mestre em
Engenharia InformáticaIntegrity constraints play an important part in database design. They are what allow
databases to store accurate information, since they impose some properties that must
always hold. However, none of the existing Database Management Systems allows the
specification of new integrity constraints if the information stored is already violating
these new integrity constraints.
In this dissertation, we developed DRSys, an application that allows the user to specify
integrity constraints that he wishes to enforce in the database. If the database becomes
inconsistent with respect to such integrity constraints, DRSys returns to the user possible ways to restore consistency, by inserting or deleting tuples into/from the original database, creating a new consistent database, a database repair. Also, since we are dealing with databases, we want to change as little information as possible, so DRSys offers the user two distinct minimality criteria when repairing the database: minimality under set inclusion or minimality under cardinality of operations.
We approached the database repairing problem by using the capacity of problem solving
offered by Answer Set Programming (ASP), which benefits from the simple specification
of problems, and the existence of “Solvers” that solve those problems in an efficient
manner.
DRSys is a database repair application that was built on top of the database management
system PostgreSQL. Furthermore, we developed a graphical user interface, to aid
the user in the whole process of defining new integrity constraints and in the process of database repairing.
We evaluate the performance and scalability of DRSys, by presenting several tests in
different situations, exploring particular features of it as well, in order to understand the scalability of DRSys
Querying and Repairing Inconsistent Prioritized Knowledge Bases: Complexity Analysis and Links with Abstract Argumentation
In this paper, we explore the issue of inconsistency handling over
prioritized knowledge bases (KBs), which consist of an ontology, a set of
facts, and a priority relation between conflicting facts. In the database
setting, a closely related scenario has been studied and led to the definition
of three different notions of optimal repairs (global, Pareto, and completion)
of a prioritized inconsistent database. After transferring the notions of
globally-, Pareto- and completion-optimal repairs to our setting, we study the
data complexity of the core reasoning tasks: query entailment under
inconsistency-tolerant semantics based upon optimal repairs, existence of a
unique optimal repair, and enumeration of all optimal repairs. Our results
provide a nearly complete picture of the data complexity of these tasks for
ontologies formulated in common DL-Lite dialects. The second contribution of
our work is to clarify the relationship between optimal repairs and different
notions of extensions for (set-based) argumentation frameworks. Among our
results, we show that Pareto-optimal repairs correspond precisely to stable
extensions (and often also to preferred extensions), and we propose a novel
semantics for prioritized KBs which is inspired by grounded extensions and
enjoys favourable computational properties. Our study also yields some results
of independent interest concerning preference-based argumentation frameworks.Comment: 27 pages. To appear in the 17th International Conference on
Principles of Knowledge Representation and Reasoning (KR 2020) without the
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