Minh Dao-Tran

Linked stream data processing engines: facts and figures

Linked Stream Data, i.e., the RDF data model extended for representing stream data generated from sensors social network applications, is gaining popularity. This has motivated considerable work on developing corresponding data models associated with processing engines. However, current implemented engines have not been thoroughly evaluated to assess their capabilities. For reasonable systematic evaluations, in this work we propose a novel, customizable evaluation framework and a corresponding methodology for realistic data generation, system testing, and result analysis. Based on this evaluation environment, extensive experiments have been conducted in order to compare the state-of-the-art LSD engines wrt. qualitative and quantitative properties, taking into account the underlying principles of stream processing. Consequently, we provide a detailed analysis of the experimental outcomes that reveal useful findings for improving current and future engines.

LARS: A logic-based framework for analyzing reasoning over streams

The recent rise of smart applications has drawn interest to logical reasoning over data streams. Different query languages and stream processing/reasoning engines were proposed. However, due to a lack of theoretical foundations, the expressivity and semantics of these diverse approaches were only informally discussed. Towards clear specifications and means for analytic study, a formal framework is needed to characterize their semantics in precise terms. We present LARS, a Logic-based framework for Analyzing Reasoning over Streams, i.e., a rule-based formalism with a novel window operator providing a flexible mechanism to represent views on streaming data. We establish complexity results for central reasoning tasks and show how the prominent Continuous Query Language (CQL) can be captured. Moreover, the relation between LARS and ETALIS, a system for complex event processing is discussed. We thus demonstrate the capability of LARS to serve as the desired formal foundation for expressing and analyzing different semantic approaches to stream processing/reasoning and engines.

Modular Nonmonotonic Logic Programming Revisited

Recently, enabling modularity aspects in Answer Set Programming (ASP) has gained increasing interest to ease the composition of program parts to an overall program. In this paper, we focus on modular nonmonotonic logic programs (MLP) under the answer set semantics, whose modules may have contextually dependent input provided by other modules. Moreover, (mutually) recursive module calls are allowed. We define a model-theoretic semantics for this extended setting, show that many desired properties of ordinary logic programming generalize to our modular ASP, and determine the computational complexity of the new formalism. We investigate the relationship of modular programs to disjunctive logic programs with well-defined input/output interface (DLP-functions) and show that they can be embedded into MLPs.

The Fourth Answer Set Programming Competition: Preliminary Report

Answer Set Programming is a well-established paradigm of declarative programming in close relationship with other declarative formalisms such as SAT Modulo Theories, Constraint Handling Rules, PDDL and many others. Since its first informal editions, ASP systems are compared in the nowadays customary ASP Competition. The fourth ASP Competition, held in 2012/2013, is the sequel to previous editions and it was jointly organized by University of Calabria Italy and the Vienna University of Technology Austria. Participants competed on a selected collection of benchmark problems, taken from a variety of research areas and real world applications. The Competition featured two tracks: the Model& Solve Track, held on an open problem encoding, on an open language basis, and open to any kind of system based on a declarative specification paradigm; and the System Track, held on the basis of fixed, public problem encodings, written in a standard ASP language.

Realizing Default Logic over Description Logic Knowledge Bases

We consider a realization of Reiter-style default logic on top of description logic knowledge bases (DL-KBs). To this end, we present elegant transformations from default theories to conjunctive query (cq-)programs that combine rules and ontologies, based on different methods to find extensions of default theories. The transformations, which are implemented in a front-end to a DL-reasoner, exploit additional constraints to prune the search space via relations between default conclusions and justifications. The front-end is a flexible tool for customizing the realization, allowing to develop alternative or refined default semantics. To our knowledge, no comparable implementation is available.

The DMCS solver for distributed nonmonotonic multi-context systems

The DMCS system is an implementation of the equilibrium semantics for heterogeneous and nonmonotonic multi-context systems (MCS) [3], which feature contexts with heterogeneous and possibly nonmonotonic logics. Each context in an MCS comprises of two parts: a local knowledge base and a set of bridge rules that can access the beliefs of other contexts and add new information to the knowledge base. In this setting, contexts are loosely coupled, and may model distributed information linkage applications; thus it is natural to have a system that allows for the distributed evaluation of MCS.

Combining nonmonotonic knowledge bases with external sources

The developments in information technology during the last decade have been rapidly changing the possibilities for data and knowledge access. To respect this, several declarative knowledge representation formalisms have been extended with the capability to access data and knowledge sources that are external to a knowledge base. This article reviews some of these formalisms that are centered around Answer Set Programming, viz. HEX-programs, modular logic programs, and multi-context systems, which were developed by the KBS group of the Vienna University of Technology in cooperation with external colleagues. These formalisms were designed with different principles and four different settings, and thus have different properties and features; however, as argued, they are not unrelated. Furthermore, they provide a basis for advanced knowledge-based information systems, which are targeted in ongoing research projects.

Decomposition of distributed nonmonotonic multi-context systems

Multi-Context Systems (MCS) are formalisms that enable the inter-linkage of single knowledge bases, called contexts, via bridge rules. Recently, a fully distributed algorithm for evaluating heterogeneous, nonmonotonic MCS was described in [7]. In this paper, we continue this line of work and present a decomposition technique for MCS which analyzes the topology of an MCS. It applies pruning techniques to get economically small representations of context dependencies. Orthogonal to this, we characterize minimal interfaces for information exchange between contexts, such that data transmissions can be minimized. We then present a novel evaluation algorithm that operates on a query plan which is compiled with topology pruning and interface minimization. The effectiveness of the optimization techniques is demonstrated by a prototype implementation, which uses an off-the-shelf SAT solver and shows encouraging experimental results.

OMiGA: an open minded grounding on-the-fly answer set solver

We present a new solver for Answer-Set Programs whose main features include grounding on-the-fly and readiness for use in solving distributed answer-set programs. The solver is implemented in Java and uses an underlying Rete network for propagation. Initial experimental results show the benefit of using Rete for this purpose, but also exhibit the need for learning in the presence of grounding on-the-fly.

A Query Model to Capture Event Pattern Matching in RDF Stream Processing Query Languages

The current state of the art in RDF Stream Processing RSP proposes several models and implementations to combine Semantic Web technologies with Data Stream Management System DSMS operators like windows. Meanwhile, only a few solutions combine Semantic Web and Complex Event Processing CEP, which includes relevant features, such as identifying sequences of events in streams. Current RSP query languages that support CEP features have several limitations: EP-SPARQL can identify sequences, but its selection and consumption policies are not all formally defined, while C-SPARQL offers only a naive support to pattern detection through a timestamp function. In this work, we introduce an RSP query language, called RSEP-QL, which supports both DSMS and CEP operators, with a special interest in formalizing CEP selection and consumption policies. We show that RSEP-QL captures EP-SPARQL and C-SPARQL, and offers features going beyond the ones provided by current RSP query languages.