A Practically Efficient Algorithm for Generating Answers to Keyword Search over Data Graphs

In keyword search over a data graph, an answer is a non-redundant subtree that contains all the keywords of the query. A naive approach to producing all the answers by increasing height is to generalize Dijkstra's algorithm to enumerating all acyclic paths by increasing weight. The idea of freezing is introduced so that (most) non-shortest paths are generated only if they are actually needed for producing answers. The resulting algorithm for generating subtrees, called GTF, is subtle and its proof of correctness is intricate. Extensive experiments show that GTF outperforms existing systems, even ones that for efficiency's sake are incomplete (i.e., cannot produce all the answers). In particular, GTF is scalable and performs well even on large data graphs and when many answers are needed.Comment: Full version of ICDT'16 pape

Automatic Termination Analysis of Programs Containing Arithmetic Predicates

For logic programs with arithmetic predicates, showing termination is not easy, since the usual order for the integers is not well-founded. A new method, easily incorporated in the TermiLog system for automatic termination analysis, is presented for showing termination in this case. The method consists of the following steps: First, a finite abstract domain for representing the range of integers is deduced automatically. Based on this abstraction, abstract interpretation is applied to the program. The result is a finite number of atoms abstracting answers to queries which are used to extend the technique of query-mapping pairs. For each query-mapping pair that is potentially non-terminating, a bounded (integer-valued) termination function is guessed. If traversing the pair decreases the value of the termination function, then termination is established. Simple functions often suffice for each query-mapping pair, and that gives our approach an edge over the classical approach of using a single termination function for all loops, which must inevitably be more complicated and harder to guess automatically. It is worth noting that the termination of McCarthy's 91 function can be shown automatically using our method. In summary, the proposed approach is based on combining a finite abstraction of the integers with the technique of the query-mapping pairs, and is essentially capable of dividing a termination proof into several cases, such that a simple termination function suffices for each case. Consequently, the whole process of proving termination can be done automatically in the framework of TermiLog and similar systems.Comment: Appeared also in Electronic Notes in Computer Science vol. 3

A General Framework for Automatic Termination Analysis of Logic Programs

This paper describes a general framework for automatic termination analysis of logic programs, where we understand by ``termination'' the finitenes s of the LD-tree constructed for the program and a given query. A general property of mappings from a certain subset of the branches of an infinite LD-tree into a finite set is proved. From this result several termination theorems are derived, by using different finite sets. The first two are formulated for the predicate dependency and atom dependency graphs. Then a general result for the case of the query-mapping pairs relevant to a program is proved (cf. \cite{Sagiv,Lindenstrauss:Sagiv}). The correctness of the {\em TermiLog} system described in \cite{Lindenstrauss:Sagiv:Serebrenik} follows from it. In this system it is not possible to prove termination for programs involving arithmetic predicates, since the usual order for the integers is not well-founded. A new method, which can be easily incorporated in {\em TermiLog} or similar systems, is presented, which makes it possible to prove termination for programs involving arithmetic predicates. It is based on combining a finite abstraction of the integers with the technique of the query-mapping pairs, and is essentially capable of dividing a termination proof into several cases, such that a simple termination function suffices for each case. Finally several possible extensions are outlined

An incremental algorithm for computing ranked full disjunctions

AbstractThe full disjunction is a variation of the join operator that maximally combines tuples from connected relations, while preserving all information in the relations. The full disjunction can be seen as a natural extension of the binary outerjoin operator to an arbitrary number of relations and is a useful operator for information integration. This paper presents the algorithm IncrementalFD for computing the full disjunction of a set of relations. IncrementalFD improves upon previous algorithms for computing the full disjunction in four ways. First, it has a lower total runtime when computing the full result and a lower runtime when computing only k tuples of the result, for any constant k. Second, for a natural class of ranking functions, IncrementalFD can be adapted to return tuples in ranking order. Third, a variation of IncrementalFD can be used to return approximate full disjunctions (which contain maximal approximately join consistent tuples). Fourth, IncrementalFD can be adapted to have a block-based execution, instead of a tuple-based execution

Validating constraints with partial information: research overview

We are interested in the problem of validating the consistency of integrity constraints when data is modified. In particular, we consider how constraints can be checked with only "partial information". Partial information may include: (1) the constraint specifications only, (2) the constraint specifications and the modified data, or (3) the constraint specifications, the modified data, and portions ofthe existing data. Methods for constraint checking with partía! ínformation can be much more efficient than traditional constraint checking methods ( e.g. because work is done at compile time, or because less data is accessed). Partial information methods also enable constraint checking in scenarios where tradítíonal constraint checking methods fail (e.g. in distributed environments where not all data is accessible). We explain how existing methods and results for query containment and for independeñce can be applied to problems (1) and (2) above, and we give an overview of our research into problem (3)

Representing and Integrating Multiple Calendars

Whenever humans refer to time, they do so with respect to a specific underlying calendar. So do most software applications. However, most theoretical models of time refer to time with respect to the integers (or reals). Thus, there is a mismatch between the theory and the application of temporal reasoning. To lessen this gap, we propose a formal, theoretical definition of a calendar and show how one may specify dates, time points, time intervals, as well as sets of time points, in terms of constraints with respect to a given calendar. Furthermore, when multiple applications using different calendars wish to work together, there is a need to integrate those calendars together into a single, unified calendar. We show how this can be done. (Also cross-referenced as UMIACS-TR-97-12

Semantic Query Optimization in Datalog Programs (Extended Abstract)

) Alon Y. Levy AT&T Bell Laboratories [email protected] Yehoshua Sagiv Hebrew University, Jerusalem [email protected] Abstract Semantic query optimization refers to the process of using integrity constraints (ic's) in order to optimize the evaluation of queries. The process is well understood in the case of unions of select-project-join queries (i.e., nonrecursive datalog). For arbitrary datalog programs, however, the issue has largely remained an unsolved problem. This paper studies this problem and shows when semantic query optimization can be completely done in recursive rules provided that order constraints and negated EDB subgoals appear only in the recursive rules, but not in the ic's. If either order constraints or negated EDB subgoals are introduced in ic's, then the problem of semantic query optimization becomes undecidable. Since semantic query optimization is closely related to the containment problem of a datalog program in a union of conjunctive queries, our res..