We study the worst-case communication complexity of the join query evaluation problem over large-scale data in distributed shared-nothing systems under the MPC model. We focus on multi-round MPC algorithms that run in constant number of rounds. The problem is well-understood for a few classes of queries, mainly the class of acyclic queries and the class of graph-like queries. For queries not belonging to either class, the complexity picture is much less clear. We study the class of degree-two queries and fragments thereof. In this paper, we tighten the gap between the upper and lower bounds for the studied classes and establish worst-case optimality for some fragments of the considered classes. We also debunk a well-believed conjecture about which query-related quantity, in the worst-case, optimally captures the communication complexity of the studied problem.

Motivated by old and new applications, we investigate Datalog as a language for sequence databases. We reconsider classical features of Datalog programs, such as negation, recursion, intermediate predicates, and relations of higher arities. We also consider new features that are useful for sequences, notably, equations between path expressions, and “packing”. Our goal is to clarify the relative expressiveness of all these different features, in the context of sequences. Towards our goal, we establish a number of redundancy and primitivity results, showing that certain features can, or cannot, be expressed in terms of other features. These results paint a complete picture of the expressiveness relationships among all possible Sequence Datalog fragments that can be formed using the six features that we consider.

The logic of information flows (LIF) has recently been proposed as a general framework in the field of knowledge representation. In this framework, tasks of procedural nature can still be modeled in a declarative, logic-based fashion. In this paper, we focus on the task of query processing under limited access patterns, a well-studied problem in the database literature. We show that LIF is well-suited for modeling this task. Toward this goal, we introduce a variant of LIF called "forward" LIF (FLIF), in a first-order setting. FLIF takes a novel graph-navigational approach; it is an XPath-like language that nevertheless turns out to be equivalent to the "executable" fragment of first-order logic defined by Nash and Lud\"ascher. One can also classify the variables in FLIF expressions as inputs and outputs. Expressions where inputs and outputs are disjoint, referred to as io-disjoint FLIF expressions, allow a particularly transparent translation into algebraic query plans that respect the access limitations. Finally, we show that general FLIF expressions can always be put into io-disjoint form.

Abstract Link traversal–based query processing (ltqp), in which a sparql query is evaluated over a web of documents rather than a single dataset, is often seen as a theoretically interesting yet impractical technique. However, in a time where the hypercentralization of data has increasingly come under scrutiny, a decentralized Web of Data with a simple document-based interface is appealing, as it enables data publishers to control their data and access rights. While (ltqp allows evaluating complex queries over such webs, it suffers from performance issues (due to the high number of documents containing data) as well as information quality concerns (due to the many sources providing such documents). In existing ltqp approaches, the burden of finding sources to query is entirely in the hands of the data consumer. In this paper, we argue that to solve these issues, data publishers should also be able to suggest sources of interest and guide the data consumer toward relevant and trustworthy data. We introduce a theoretical framework that enables such guided link traversal and study its properties. We illustrate with a theoretic example that this can improve query results and reduce the number of network requests. We evaluate our proposal experimentally on a virtual linked web with specifications and indeed observe that not just the data quality but also the efficiency of querying improves.

The logic of information flows (LIF) is a general framework in which tasks of a procedural nature can be modeled in a declarative, logic-based fashion. The first contribution of this article is to propose semantic and syntactic definitions of inputs and outputs of LIF expressions. We study how the two relate and show that our syntactic definition is optimal in a sense that is made precise. The second contribution is a systematic study of the expressive power of sequential composition in LIF. Our results on composition tie in the results on inputs and outputs and relate LIF to first-order logic (FO) and bounded-variable LIF to bounded- variable FO. This article is the extended version of a paper presented at KR 2020 [ 2 ].

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Last year we introduced the logic FLIF (forward logic of information flows) as a declarative language for specifying complex compositions of information sources with limited access patterns. The key insight of this approach is to view a system of information sources as a graph, where the nodes are valuations of variables, so that accesses to information sources can be modeled as edges in the graph. This allows the use of XPath-like navigational graph query languages. Indeed, a well-behaved fragment of FLIF, called io-disjoint FLIF, was shown to be equivalent to the executable fragment of first-order logic. It remained open, however, how io-disjoint FLIF compares to general FLIF . In this paper we close this gap by showing that general FLIF expressions can always be put into io-disjoint form.

Motivated by old and new applications, we investigate Datalog as a language for sequence databases. We reconsider classical features of Datalog programs, such as negation, recursion, intermediate predicates, and relations of higher arities. We also consider new features that are useful for sequences, notably, equations between path expressions, and "packing''. Our goal is to clarify the relative expressiveness of all these different features, in the context of sequences. Towards our goal, we establish a number of redundancy and primitivity results, showing that certain features can, or cannot, be expressed in terms of other features. These results paint a complete picture of the expressiveness relationships among all possible Sequence Datalog fragments that can be formed using the six features that we consider.

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The logic of information flows (LIF) has recently been proposed as a general framework in the field of knowledge representation. In this framework, tasks of a procedural nature can still be modeled in a declarative, logic-based fashion. In this paper, we focus on the task of query processing under limited access patterns, a well-studied problem in the database literature. We show that LIF is well-suited for modeling this task. Toward this goal, we introduce a variant of LIF called "forward" LIF, in a first-order setting. We define FLIF^io, a syntactical fragment of forward LIF, and show that it corresponds exactly to the "executable" fragment of first-order logic defined by Nash and Ludäscher. The definition of FLIF^io involves a classification of the free variables of an expression into "input" and "output" variables. Our result hinges on inertia and determinacy laws for forward LIF expressions, which are interesting in their own right. These laws are formulated in terms of the input and output variables.

The logic of information flows (LIF) is a general framework in which tasks of a procedural nature can be modeled in a declarative, logic-based fashion. The first contribution of this paper is to propose semantic and syntactic definitions of inputs and outputs of LIF expressions. We study how the two relate and show that our syntactic definition is optimal in a sense that is made precise. The second contribution of this paper is a systematic study of the expressive power of sequential composition in LIF. Our results on composition tie in the results on inputs and outputs, and relate LIF to first-order logic (FO) and bounded-variable LIF to bounded-variable FO.