Encoding Tight Typing in a Unified Framework

This paper explores how the intersection type theories of call-by-name (CBN) and call-by-value (CBV) can be unified in a more general framework provided by call-by-push-value (CBPV). Indeed, we propose tight type systems for CBN and CBV that can be both encoded in a unique tight type system for CBPV. All such systems are quantitative, i.e. they provide exact information about the length of normalization sequences to normal form as well as the size of these normal forms. Moreover, the length of reduction sequences are discriminated according to their multiplicative and exponential nature, a concept inherited from linear logic. Last but not least, it is possible to extract quantitative measures for CBN and CBV from their corresponding encodings in CBPV

The Bang Calculus Revisited

Call-by-Push-Value (CBPV) is a programming paradigm subsuming both Call-by-Name (CBN) and Call-by-Value (CBV) semantics. The paradigm was recently modelled by means of the Bang Calculus, a term language connecting CBPV and Linear Logic. This paper presents a revisited version of the Bang Calculus, called $\lambda !$, enjoying some important properties missing in the original system. Indeed, the new calculus integrates commutative conversions to unblock value redexes while being confluent at the same time. A second contribution is related to non-idempotent types. We provide a quantitative type system for our $\lambda !$-calculus, and we show that the length of the (weak) reduction of a typed term to its normal form \emph{plus} the size of this normal form is bounded by the size of its type derivation. We also explore the properties of this type system with respect to CBN/CBV translations. We keep the original CBN translation from $\lambda$-calculus to the Bang Calculus, which preserves normal forms and is sound and complete with respect to the (quantitative) type system for CBN. However, in the case of CBV, we reformulate both the translation and the type system to restore two main properties: preservation of normal forms and completeness. Last but not least, the quantitative system is refined to a \emph{tight} one, which transforms the previous upper bound on the length of reduction to normal form plus its size into two independent \emph{exact} measures for them

The construction of oracles for software testing

Software testing is important throughout the software life cycle. Testing is the part of the software development process where a computer program is subject to specific conditions to show that the problem meets its intended design. Building a testing oracle is one part of software testing. An oracle is an external mechanism which can be used to check test output for correctness. The characteristics of available oracles have a dominating influence on the cost and quality of software testing. In this thesis, methods of constructing oracles are investigated and classified. There are three kinds of method of constructing oracles: the pseudo-oracle approach, oracles using attributed grammars and oracles based on formal specification. This thesis develops a method for constructing an oracle, based on the Z specification language. A specification language can describe the correct syntax and semantics of software. The contextual part of a specification describes all the legal input to the program and the semantics part describes the meaning of the given input data. Based on this idea, an oracle is constructed and a prototype is implemented according to the method proposed in the thesis

Dynamic Privacy Management In Services Based Interactions

Technology advancements have enabled the distribution and sharing of users personal data over several data sources. Each data source is potentially managed by a different organization, which may expose its data as a Web service. Using such Web services, dynamic composition of atomic data items coupled with the context in which the data is accessed may breach sensitive data that may not comply with the users preference at the time of data collection. Thus, providing uniform access policies to such data can lead to privacy problems. Some fairly recent research has focused on providing solutions for dynamic privacy management. This thesis advances these techniques, and fills some gaps in the existing works. In particular, dynamically incorporating user access context into the privacy policy decision, and its enforcement

SEMEDA (Semantic Meta-Database) : ontology based semantic integration of biological databases

Köhler J. SEMEDA (Semantic Meta-Database) : ontology based semantic integration of biological databases. Bielefeld (Germany): Bielefeld University; 2003.The work presented in this thesis is outlined in the following. The state of the art in the relevant disciplines is introduced and reviewed in chapter 2. This includes on the one hand the current state of molecular biological databases, their heterogeneity and the integration of molecular biological databases. On the other hand the current usage of ontologies in general and with special regard to database integration is described. The principles of semantic database integration as introduced in this thesis are new and suitable to be used also in other database integration systems, which have to deal with a high number of semantically heterogeneous databases. Therefore in Chapter 3 the newly introduced principles for ontology based semantic database integration are presented independent of their implementation. Chapter 4 introduces the requirements for the implementation of a semantic database integration system (SEMEDA). Several general requirements for the integration of molecular biological systems from the scientific literature are discussed with regard to the feasibility of their implementation in general and in SEMEDA. In addition, the requirements specific to semantic database integration are introduced. In addition how the BioDataServer is used to overcome "technical" heterogeneity, so that SEMEDA only has to deal with semantic heterogeneity is analysed. In chapter 5, an appropriate data structure for storing ontologies, database metadata and the semantic definitions as described in Chapter 3 is developed. Subsequently, it is discussed how this data structure can be edited and queried. In Chapter 6, SEMEDAs software design, implementation and system architecture is given. Chapter 7 describes the use of SEMEDA and its interfaces. The user interface SEMEDA-edit is used to collaboratively edit ontologies and to semantically define databases using ontologies. SEMEDA-query is the query interface that provides uniform access to heterogeneous databases. In addition, a set of procedures exists which can be used by external applications. In order to use SEMEDA to semantically define databases, an appropriate ontology is needed. Although SEMEDA allows building ontologies from the scratch, due to the fact that generating ontologies is a labour intensive time-consuming task, it would be preferable to use an existing ontology. Therefore, in chapter 8 several ontologies were evaluated for their usability in SEMEDA. The intention was to find out if a suitable ontology can be found and imported or whether it is more appropriate to build a custom ontology for SEMEDA. It turned out that the existing ontologies were not well suited for semantic database integration. In chapter 9 general and SEMEDA specific ontology design principles are introduced which were then followed to build a custom ontology for database integration. The structure of this custom ontology and some issues concerning its use for semantic database integration are explained. In chapter 10, the practical use of SEMEDA is described by two examples. The first section of this chapter shows how SEMEDA supports the building of user schemata for the BioDataServer. The second section describes how the clone database of the RZPD Berlin (Deutsches Ressourcenzentrum für Genomforschung GmbH) is connected to SEMEDA and thus linked to the other databases. In the discussion (chapter 11) SEMEDA is compared to existing database integration systems, especially other ontology based integration systems. It is further discussed how principles for semantic database integration apply to other database integration systems and how they might be implemented there. A database mirror is proposed to improve the overall performance of SEMEDA and the BioDataServer