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Tableaux for Reasoning About Atomic Updates
, 2001
"... A simple model of dynamic databases is studied from a modal logic perspecitve. A state a of a database is an atomic update of a state b if at most one atomic statement is evaluated differently in a compared to b. The corresponding restriction on Kripkelike structures yields socalled update logi ..."
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A simple model of dynamic databases is studied from a modal logic perspecitve. A state a of a database is an atomic update of a state b if at most one atomic statement is evaluated differently in a compared to b. The corresponding restriction on Kripkelike structures yields socalled update
Hyper Tableaux and Disjunctive Logic Programming
 FACHBERICHTE INFORMATIK 1396, UNIVERSITAT KOBLENZLANDAU, UNIVERSITAT KOBLENZLANDAU, INSTITUT FUR INFORMATIK, RHEINAU 1, D56075
, 1996
"... ... This paper proves that there exist an efficient proof procedure, namely hyper tableaux, which can be understood as a direct implementation of some of the well known fixpoint iteration techniques. We show how a hyper tableaux refutation can be transformed into a restart model elimination refutati ..."
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Cited by 1 (1 self)
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over states. We will relate hyper tableaux to this iteration. Another approach by Fernandez and Minker ( [ Fernandez and Minker, 91 ] ), gives a bottom up evaluation of hierarchical disjunctive databases. We will demonstrate, that this approach is a special case of hyper tableaux. In Section 5 we
Consistency Checking in Complex Object Database Schemata with Integrity Constraints
, 1998
"... Integrity constraints are rules which should guarantee the integrity of a database. Provided that an adequate mechanism to express them is available, the following question arises: is there any way to populate a database which satisfies the constraints supplied by a database designer? i.e., does the ..."
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Cited by 23 (13 self)
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the database schema, including constraints, admit at least a nonempty model? This work gives an answer to the above question in a complex object database environment, providing a theoretical framework including the following ingredients: two alternative formalisms, able to express a relevant set of state
Permanents, Transportation Polytopes and Positive Definite Kernels on Histograms
, 2007
"... For two integral histograms r =(r1,...,rd) and c = (c1,...,cd) of equal sum N, the MongeKantorovich distance dMK(r, c) between r and c parameterized by a d × d distance matrix T is the minimum of all costs <F,T>taken over matrices F of the transportation polytope U(r, c). Recent results sugge ..."
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Cited by 4 (0 self)
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based on the whole transportation polytope U(r, c). We prove that when r and c have binary counts, which is equivalent to stating that r and c represent clouds of points of equal size, the permanent of an adequate Gram matrix induced by the distance matrix T is a positive definite kernel under favorable
Automated reasoning in modal and description logics via SAT encoding: the case study of k(m)/alcsatisfiability.
 J. Artif. Intell. Res. (JAIR)
, 2009
"... Abstract In the last two decades, modal and description logics have been applied to numerous areas of computer science, including knowledge representation, formal verification, database theory, distributed computing and, more recently, semantic web and ontologies. For this reason, the problem of au ..."
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Cited by 6 (2 self)
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Abstract In the last two decades, modal and description logics have been applied to numerous areas of computer science, including knowledge representation, formal verification, database theory, distributed computing and, more recently, semantic web and ontologies. For this reason, the problem
Chapter 12 Rough Sets and Rough Logic: A KDD Perspective
"... Abstract Basic ideas of rough set theory were proposed by Zdzis law Pawlak [85, 86] in the early 1980’s. In the ensuing years, we have witnessed a systematic, world–wide growth of interest in rough sets and their applications. The main goal of rough set analysis is induction of approximations of con ..."
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Abstract Basic ideas of rough set theory were proposed by Zdzis law Pawlak [85, 86] in the early 1980’s. In the ensuing years, we have witnessed a systematic, world–wide growth of interest in rough sets and their applications. The main goal of rough set analysis is induction of approximations of concepts. This main goal is motivated by the basic fact, constituting also the main problem of KDD, that languages we may choose for knowledge description are incomplete. A fortiori, we have to describe concepts of interest (features, properties, relations etc.) not completely but by means of their reflections (i.e. approximations) in the chosen language. The most important issues in this induction process are: – construction of relevant primitive concepts from which approximations of more complex concepts are assembled, – measures of inclusion and similarity (closeness) on concepts, – construction of operations producing complex concepts from the primitive ones.