Towards Aggregated Answers for Semistructured Data

Meuss, Holger; Schulz, Klaus U.; Bry, François

doi:10.1007/3-540-44503-x_22

Cited by 14 publications

(16 citation statements)

References 18 publications

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“…As already observed in Proposition 3, while full Datalog is EXPTIME-complete (see, e.g., [14]), monadic Datalog over arbitrary finite structures is in NP (actually, NP-complete). For a lower bound on trees, it is known [41] that already Boolean conjunctive queries over structures of the form (P i ) i , child, child * are NP-hard w.r.t. combined complexity.…”

Section: The Complexity Of Xpath Query Evaluationmentioning

confidence: 99%

Logic, Languages, and Rules for Web Data Extraction and Reasoning over Data

Gottlob

Koch

Pieris

2017

Language and Automata Theory and Applications

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Abstract. This paper gives a short overview of specific logical approaches to data extraction, data management, and reasoning about data. In particular, we survey theoretical results and formalisms that have been obtained and used in the context of the Lixto Project at TU Wien, the DIADEM project at the University of Oxford, and the VADA project, which is currently being carried out jointly by the universities of Edinburgh, Manchester, and Oxford. We start with a formal approach to web data extraction rooted in monadic second order logic and monadic Datalog, that gave rise to the Lixto data extraction system. We then present some complexity results for monadic Datalog over trees and for XPath query evaluation. We further argue that for value-creation and for ontological reasoning over data, we need existential quantifiers (or Skolem terms) in rule heads, and introduce the Datalog ± family. We give an overview of important members of this family and discuss related complexity issues.

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Section: The Complexity Of Xpath Query Evaluationmentioning

confidence: 99%

Logic, Languages, and Rules for Web Data Extraction and Reasoning over Data

Gottlob

Koch

Pieris

2017

Language and Automata Theory and Applications

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“…Related Work SRED has much similarity to the Complete Answer Aggregate (CAA) introduced by Meuss, Schulz, and Bry [7] as a compact representation of answer sets of queries. The size of a CAA is O(nh|t|) which is competitive to our O(3 n |t|).…”

Section: Introductionmentioning

confidence: 99%

“…CAA is also suitable for applying several operations such as membership testing. The main advantage of our work is that it supports arbitrary regular queries, which is strictly more expressive than the query language used in [7]. Though an attempt to represent the answer sets of regular queries with CAA is given by Filiot and Tison [8] through a decomposition of queries, the space complexity is O(n|t| d ϕ ) for some constant d ϕ depending on the query, which grows to n in the worst case.…”

Section: Introductionmentioning

confidence: 99%

Compact Representation for Answer Sets of n-ary Regular Queries

Inaba

Hosoya

2009

Implementation and Application of Automata

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Abstract. An n-ary query over trees takes an input tree t and returns a set of ntuples of the nodes of t. In this paper, a compact data structure is introduced for representing the answer sets of n-ary queries defined by tree automata. Despite that the number of the elements of the answer set can be as large as |t| n , our representation allows to store the set using only O(3 n |t|) space. Several basic operations on the sets are shown to be efficiently executable on the representation.

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“…In particular, if the variables are independent, this allows to represent the set of answers as a Cartesian product of sets of (sub)answers of lower sizes. More generally, aggregated answers have been introduced in [14] as a compact representation of the set of answers. Basically, these are multipartite dags such that each part corresponds to a free variable, and the branching structure of the parts is a tree.…”

Section: Fig 1 a Tree Representing A Directorymentioning

confidence: 99%

“…A person may have several emails, making this representation more compact than the set of all answers. Moreover, as argued in [14], this representation keeps the information on how the components of the output tuples are related in the tree. It is also particularly appropriate for post-processing tasks, such as answer searching, answer browsing, statistical computing, answer enumeration, and cascade-style querying.…”

Section: Fig 1 a Tree Representing A Directorymentioning

confidence: 99%

Regular n-ary Queries in Trees and Variable Independence

Filiot

Tison

Fifth Ifip International Conference on Theoretical Computer Science – TCS 2008

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Abstract. Regular n-ary queries in trees are queries which are definable by an MSO formula with n free first-order variables. We investigate the variable independence problem -originally introduced for databases -in the context of trees. In particular, we show how to decide whether a regular query is equivalent to a union of cartesian products, independently of the input tree. As an intermediate step, we reduce this problem to the problem of deciding whether the number of answers to a regular query is bounded by some constant, independently of the input tree. As a (non-trivial) generalization, we introduce variable independence w.r.t. a dependence forest between blocks of variables, which we prove to be decidable.

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Towards Aggregated Answers for Semistructured Data

Cited by 14 publications

References 18 publications

Logic, Languages, and Rules for Web Data Extraction and Reasoning over Data

Logic, Languages, and Rules for Web Data Extraction and Reasoning over Data

Compact Representation for Answer Sets of n-ary Regular Queries

Regular n-ary Queries in Trees and Variable Independence

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