An algebraic formulation of the aggregative closure query

AbstractThe aggregative closure problem, a transitive closure problem with aggregations on transitive paths, is formally defined by database terms. Its definition in our paper holds only on the subset conditions of path algebra, thereby it is more general than other definitions in previous works. For the completion of the definition, we suggest conditions for the existence of the fixpoint and classified the conditions as the properties of the aggregate operators and the problem domain. So we can verify the existence of the fixpoint by the suggested conditions. The naive algorithm is proposed as a computational semantics for the aggregative closure problem. This study also proves that for an aggregative closure problem the semi-naive algorithm is computationally equivalent to the naive algorithm when the aggregate product operator is distributive over aggregate sum operator

A survey of parallel execution strategies for transitive closure and logic programs

An important feature of database technology of the nineties is the use of parallelism for speeding up the execution of complex queries. This technology is being tested in several experimental database architectures and a few commercial systems for conventional select-project-join queries. In particular, hash-based fragmentation is used to distribute data to disks under the control of different processors in order to perform selections and joins in parallel. With the development of new query languages, and in particular with the definition of transitive closure queries and of more general logic programming queries, the new dimension of recursion has been added to query processing. Recursive queries are complex; at the same time, their regular structure is particularly suited for parallel execution, and parallelism may give a high efficiency gain. We survey the approaches to parallel execution of recursive queries that have been presented in the recent literature. We observe that research on parallel execution of recursive queries is separated into two distinct subareas, one focused on the transitive closure of Relational Algebra expressions, the other one focused on optimization of more general Datalog queries. Though the subareas seem radically different because of the approach and formalism used, they have many common features. This is not surprising, because most typical Datalog queries can be solved by means of the transitive closure of simple algebraic expressions. We first analyze the relationship between the transitive closure of expressions in Relational Algebra and Datalog programs. We then review sequential methods for evaluating transitive closure, distinguishing iterative and direct methods. We address the parallelization of these methods, by discussing various forms of parallelization. Data fragmentation plays an important role in obtaining parallel execution; we describe hash-based and semantic fragmentation. Finally, we consider Datalog queries, and present general methods for parallel rule execution; we recognize the similarities between these methods and the methods reviewed previously, when the former are applied to linear Datalog queries. We also provide a quantitative analysis that shows the impact of the initial data distribution on the performance of methods

Models and Algorithms for Persistent Queries over Streaming Graphs

It is natural to model and represent interaction data as graphs in a broad range of domains such as online social networks, protein interaction data, and e-commerce applications. A number of emerging applications require continuous processing and querying of interaction data that evolves at a high rate, in near real-time, which can be modelled as a streaming graph. Persistent queries, where queries are registered into the system and new results are generated incrementally as the graph edges arrive, facilitate online analysis and real-time monitoring over streaming data. Processing persistent queries over streaming graphs combines two seemingly different but challenging problems: graph querying and streaming processing. Existing systems fail to support these workloads due to (i) the complexity of graph queries that feature recursive path navigations, subgraph patterns, and path manipulation, and (ii) the unboundedness and growth rate of streaming graphs that make it infeasible to employ batch algorithms. Consequently, a growing number of applications rely on specialized solutions tailored to specific application needs. This thesis introduces foundational techniques for efficient processing of persistent queries over streaming graphs to support this emerging class of applications in a principled manner. The main contribution of this thesis is the design and development of a general-purpose streaming graph query processing framework. The novel challenges of persistent queries over streaming graphs dictate rethinking the components of the well-established query processor architecture, and this thesis introduces the models and algorithms to address these challenges uniformly. The central notion of Streaming Graph Query precisely characterizes the semantics of persistent queries over streaming graphs, making it possible to reason about the expressiveness and the complexity of queries targeted by the aforementioned applications. Streaming Graph Algebra, defined as a closure of a set of operators over streaming graphs, provides the primitive building blocks for evaluating and optimizing streaming graph queries. Efficient, incremental algorithms as the physical implementations of streaming graph algebra operators are provided, enabling streaming graph queries to be evaluated in a data-driven fashion. It is shown that the proposed algebra constitutes the foundational tool for the cost-based optimization of streaming graph queries by providing an algebraic basis for query evaluation. Overall, this thesis provides principled solutions to fundamental challenges for efficient querying of streaming graphs and describes the design and implementation of a general-purpose streaming graph query processing framework

FCAIR 2012 Formal Concept Analysis Meets Information Retrieval Workshop co-located with the 35th European Conference on Information Retrieval (ECIR 2013) March 24, 2013, Moscow, Russia

International audienceFormal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classifiation. The area came into being in the early 1980s and has since then spawned over 10000 scientific publications and a variety of practically deployed tools. FCA allows one to build from a data table with objects in rows and attributes in columns a taxonomic data structure called concept lattice, which can be used for many purposes, especially for Knowledge Discovery and Information Retrieval. The Formal Concept Analysis Meets Information Retrieval (FCAIR) workshop collocated with the 35th European Conference on Information Retrieval (ECIR 2013) was intended, on the one hand, to attract researchers from FCA community to a broad discussion of FCA-based research on information retrieval, and, on the other hand, to promote ideas, models, and methods of FCA in the community of Information Retrieval

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Fitting aggregation operators to data

Theoretical advances in modelling aggregation of information produced a wide range of aggregation operators, applicable to almost every practical problem. The most important classes of aggregation operators include triangular norms, uninorms, generalised means and OWA operators.With such a variety, an important practical problem has emerged: how to fit the parameters/ weights of these families of aggregation operators to observed data? How to estimate quantitatively whether a given class of operators is suitable as a model in a given practical setting? Aggregation operators are rather special classes of functions, and thus they require specialised regression techniques, which would enforce important theoretical properties, like commutativity or associativity. My presentation will address this issue in detail, and will discuss various regression methods applicable specifically to t-norms, uninorms and generalised means. I will also demonstrate software implementing these regression techniques, which would allow practitioners to paste their data and obtain optimal parameters of the chosen family of operators.<br /

Reasoning with Inconsistent Information

In this thesis we are concerned with developing formal and representational mechanisms for reasoning with inconsistent information. Strictly speaking there are two conceptually distinct senses in which we are interested in reasoning with inconsistent information. In one sense, we are interested in using logical deduction to draw inferences in a symbolic system. More specifically, we are interested in mechanisms that can continue to perform deduction in a reasonable manner despite the threat of inconsistencies as a direct result of errors or misrepresentations. So in this sense we are interested in inconsistency-tolerant or paraconsistent deduction. … ¶ In this thesis we adopt a novel framework to unify both logic-as-deduction and logic-as-representation approaches to reasoning with inconsistent information. …