Computing Horn Rewritings of Description Logics Ontologies

We study the problem of rewriting an ontology O1 expressed in a DL L1 into an ontology O2 in a Horn DL L2 such that O1 and O2 are equisatisfiable when extended with an arbitrary dataset. Ontologies that admit such rewritings are amenable to reasoning techniques ensuring tractability in data complexity. After showing undecidability whenever L1 extends ALCF, we focus on devising efficiently checkable conditions that ensure existence of a Horn rewriting. By lifting existing techniques for rewriting Disjunctive Datalog programs into plain Datalog to the case of arbitrary first-order programs with function symbols, we identify a class of ontologies that admit Horn rewritings of polynomial size. Our experiments indicate that many real-world ontologies satisfy our sufficient conditions and thus admit polynomial Horn rewritings.Comment: 15 pages. To appear in IJCAI-1

Datalog Rewritability of Disjunctive Datalog Programs and its Applications to Ontology Reasoning

We study the problem of rewriting a disjunctive datalog program into plain datalog. We show that a disjunctive program is rewritable if and only if it is equivalent to a linear disjunctive program, thus providing a novel characterisation of datalog rewritability. Motivated by this result, we propose weakly linear disjunctive datalog---a novel rule-based KR language that extends both datalog and linear disjunctive datalog and for which reasoning is tractable in data complexity. We then explore applications of weakly linear programs to ontology reasoning and propose a tractable extension of OWL 2 RL with disjunctive axioms. Our empirical results suggest that many non-Horn ontologies can be reduced to weakly linear programs and that query answering over such ontologies using a datalog engine is feasible in practice.Comment: 14 pages. To appear at AAAI-1

A Framework for Automatic SLA Creation

Negotiation is fundamental to business. Increased automation of business to business or business to customer interaction is demanding efficient but flexible systems that can manage the negotiation process with minimal direct human intervention. Industries that provide online services rely on Service Level Agreements as the basis for their contractual relationship. Here we look at a means for generating these with a negotiating tool (SLA Negotiation Manager) that complies with e-negotiation rules and creates the agreements from existing business objectives

SLA Automated Negotiation Manager for Computing Services

Success in today’s marketing arena can often depend on companies embracing effective new technologies and integrating them into their business model. In the computing service supply industry, Service Level Agreements (SLAs) are commonly prepared and signed agreements between the service provider and its customers. SLAs should match business needs of both sides of the agreement as closely as possible. This paper focuses on at the steps and activities that the service provider can take to facilitate agreement. It proposes an automated way for creating SLA’s from a set of Service Level Objectives (SLOs). The SLA should achieve business goals, including the maximization of customer satisfaction. To automate the preparation of effective SLAs each company should set SLOs that support business needs

Employing Intelligent Agents to Automate SLA Creation

Service Level Agreements (SLAs) are commonly prepared and signed agreements that form the contracts between a service provider and its customers, defining the obligations and liabilities of the parties. Naturally, SLAs should reflect the business needs of both customer and supplier. SLAs are usually formed through either the adoption of a boilerplate agreement from the provider, or through a mediation/negotiation process between the parties. With the increasing adoption of software supply being implemented as a network service, such schemes are rigid or slow and costly, This paper proposes a system that the parties can use to facilitate both fast and flexible agreements. It proposes automation of SLA creation from a set of Service Level Objectives (SLOs), making use of software agents and adopting a social order function by incorporating it into the decision process

Verifiable Electronic Voting System: An Open Source Solution

Elections, referenda and polls are vital processes for the operation of a modern democracy. They form the mechanism for transferring power from citizens to their representatives. Although some commentators claim that the pencil-and-paper systems used in countries such as Canada and UK are still the best method of avoiding voterigging, recent election problems, and the need for faster, better, cheaper vote counting, have stimulated great interest in managing the election process through the use of electronic voting systems. While computer scientists, for the most part, have been warning of the possible perils of such action, vendors have forged ahead with their products, claiming increased security and reliability. Many democracies have adopted electronic systems, and the number of deployed systems is rising. Although the electronic voting process has gained popularity and users, it is a great challenge to provide a reliable system. The existing systems available to perform the election tasks are far from trustworthy. In this paper we describe VEV (Verifiable E-Voting), an electronic voting system which is opne, but also provides for secret and secure voting, and can be used and verified over existing network system

Stream Reasoning in Temporal Datalog

In recent years, there has been an increasing interest in extending traditional stream processing engines with logical, rule-based, reasoning capabilities. This poses significant theoretical and practical challenges since rules can derive new information and propagate it both towards past and future time points; as a result, streamed query answers can depend on data that has not yet been received, as well as on data that arrived far in the past. Stream reasoning algorithms, however, must be able to stream out query answers as soon as possible, and can only keep a limited number of previous input facts in memory. In this paper, we propose novel reasoning problems to deal with these challenges, and study their computational properties on Datalog extended with a temporal sort and the successor function (a core rule-based language for stream reasoning applications)

Foundations of Declarative Data Analysis Using Limit Datalog Programs

Motivated by applications in declarative data analysis, we study $\mathit{Datalog}_{\mathbb{Z}}$---an extension of positive Datalog with arithmetic functions over integers. This language is known to be undecidable, so we propose two fragments. In $\mathit{limit}~\mathit{Datalog}_{\mathbb{Z}}$ predicates are axiomatised to keep minimal/maximal numeric values, allowing us to show that fact entailment is coNExpTime-complete in combined, and coNP-complete in data complexity. Moreover, an additional $\mathit{stability}$ requirement causes the complexity to drop to ExpTime and PTime, respectively. Finally, we show that stable $\mathit{Datalog}_{\mathbb{Z}}$ can express many useful data analysis tasks, and so our results provide a sound foundation for the development of advanced information systems.Comment: 23 pages; full version of a paper accepted at IJCAI-17; v2 fixes some typos and improves the acknowledgment