Extended Dualization: Application to Maximal Pattern Mining

International audienceThe dualization in arbitrary posets is a well-studied problem in combinatorial enumeration and is a crucial step in many applications in logics, databases, artificial intelligence and pattern mining.The objective of this paper is to study reductions of the dualization problem on arbitrary posets to the dualization problem on boolean lattices, for which output quasi-polynomial time algorithms exist. Quasi-polynomial time algorithms are algorithms which run in no(logn) where n is the size of the input and output. We introduce convex embedding and poset reflection as key notions to characterize such reductions. As a consequence, we identify posets, which are not boolean lattices, for which the dualization problem remains in quasi-polynomial time and propose a classification of posets with respect to dualization.From these results, we study how they can be applied to maximal pattern mining problems. We deduce a new classification of pattern mining problems and we point out how known problems involving sequences and conjunctive queries patterns, fit into this classification. Finally, we explain how to adapt the seminal Dualize & Advance algorithm to deal with such patterns.As far as we know, this is the first contribution to explicit non-trivial reductions for studying the hardness of maximal pattern mining problems and to extend the Dualize & Advance algorithm for complex patterns

Dealing with Inconsistency and Incompleteness in Data Integration

Marco Schaerf, Giuseppe Di Battista, Domenico SaccÃ

Inductive Logic Programming in Databases: from Datalog to DL+log

In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e. the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through a better understanding of KR aspects related to databases. In particular, we investigate this issue from the ILP perspective by considering two database problems, (i) the definition of views and (ii) the definition of constraints, for a database whose schema is represented also by means of an ontology. Both can be reformulated as ILP problems and can benefit from the expressive and deductive power of the KR framework DL+log. We illustrate the application scenarios by means of examples. Keywords: Inductive Logic Programming, Relational Databases, Ontologies, Description Logics, Hybrid Knowledge Representation and Reasoning Systems. Note: To appear in Theory and Practice of Logic Programming (TPLP).Comment: 30 pages, 3 figures, 2 tables

Four Lessons in Versatility or How Query Languages Adapt to the Web

Exposing not only human-centered information, but machine-processable data on the Web is one of the commonalities of recent Web trends. It has enabled a new kind of applications and businesses where the data is used in ways not foreseen by the data providers. Yet this exposition has fractured the Web into islands of data, each in different Web formats: Some providers choose XML, others RDF, again others JSON or OWL, for their data, even in similar domains. This fracturing stifles innovation as application builders have to cope not only with one Web stack (e.g., XML technology) but with several ones, each of considerable complexity. With Xcerpt we have developed a rule- and pattern based query language that aims to give shield application builders from much of this complexity: In a single query language XML and RDF data can be accessed, processed, combined, and re-published. Though the need for combined access to XML and RDF data has been recognized in previous work (including the W3C’s GRDDL), our approach differs in four main aspects: (1) We provide a single language (rather than two separate or embedded languages), thus minimizing the conceptual overhead of dealing with disparate data formats. (2) Both the declarative (logic-based) and the operational semantics are unified in that they apply for querying XML and RDF in the same way. (3) We show that the resulting query language can be implemented reusing traditional database technology, if desirable. Nevertheless, we also give a unified evaluation approach based on interval labelings of graphs that is at least as fast as existing approaches for tree-shaped XML data, yet provides linear time and space querying also for many RDF graphs. We believe that Web query languages are the right tool for declarative data access in Web applications and that Xcerpt is a significant step towards a more convenient, yet highly efficient data access in a “Web of Data”

Data Integration Driven Ontology Design, Case Study Smart City

Methods to design of formal ontologies have been in focus of research since the early nineties when their importance and conceivable practical application in engineering sciences had been understood. However, often significant customization of generic methodologies is required when they are applied in tangible scenarios. In this paper, we present a methodology for ontology design developed in the context of data integration. In this scenario, a targeting ontology is applied as a mediator for distinct schemas of individual data sources and, furthermore, as a reference schema for federated data queries. The methodology has been used and evaluated in a case study aiming at integration of buildings' energy and carbon emission related data. We claim that we have made the design process much more efficient and that there is a high potential to reuse the methodology

Physical Design for Non-relational Data Systems

Decades of research have gone into the optimization of physical designs, query execution, and related tools for relational databases. These techniques and tools make it possible for non-expert users to make effective use of relational database management systems. However, the drive for flexible data models and increased scalability has spawned a new generation of data management systems which largely eschew the relational model. These include systems such as NoSQL databases and distributed analytics frameworks such as Apache Spark which make use of a diverse set of data models. Optimization techniques and tools developed for relational data do not directly apply in this setting. This leaves developers making use of these systems with the need to become intimately familiar with system details to obtain good performance. We present techniques and tools for physical design for non-relational data systems. We explore two settings: NoSQL database systems and distributed analytics frameworks. While NoSQL databases often avoid explicit schema definitions, many choices on how to structure data remain. These choices can have a significant impact on application performance. The data structuring process normally requires expert knowledge of the underlying database. We present the NoSQL Schema Evaluator (NoSE). Given a target workload, NoSE provides an optimized physical design for NoSQL database applications which compares favourably to schemas designed by expert users. To enable existing applications to benefit from conceptual modeling, we also present an algorithm to recover a logical model from a denormalized database instance. Our second setting is distributed analytics frameworks such as Apache Spark. As is the case for NoSQL databases, expert knowledge of Spark is often required to construct efficient data pipelines. In NoSQL systems, a key challenge is how to structure stored data, while in Spark, a key challenge is how to cache intermediate results. We examine a particularly common scenario in Spark which involves performing iterative analysis on an input dataset. We show that jobs written in an intuitive manner using existing Spark APIs can have poor performance. We propose ReSpark, which automates caching decisions for iterative Spark analyses. Like NoSE, ReSpark makes it possible for non-expert users to obtain good performance from a non-relational data system

Query Answering in Probabilistic Data and Knowledge Bases

Probabilistic data and knowledge bases are becoming increasingly important in academia and industry. They are continuously extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic database systems, which are widely and successfully employed. Beyond all the success stories, however, such systems still lack the fundamental machinery to convey some of the valuable knowledge hidden in them to the end user, which limits their potential applications in practice. In particular, in their classical form, such systems are typically based on strong, unrealistic limitations, such as the closed-world assumption, the closed-domain assumption, the tuple-independence assumption, and the lack of commonsense knowledge. These limitations do not only lead to unwanted consequences, but also put such systems on weak footing in important tasks, querying answering being a very central one. In this thesis, we enhance probabilistic data and knowledge bases with more realistic data models, thereby allowing for better means for querying them. Building on the long endeavor of unifying logic and probability, we develop different rigorous semantics for probabilistic data and knowledge bases, analyze their computational properties and identify sources of (in)tractability and design practical scalable query answering algorithms whenever possible. To achieve this, the current work brings together some recent paradigms from logics, probabilistic inference, and database theory