Efficient data representation for XML in peer-based systems

Purpose - New directions in the provision of end-user computing experiences mean that the best way to share data between small mobile computing devices needs to be determined. Partitioning large structures so that they can be shared efficiently provides a basis for data-intensive applications on such platforms. The partitioned structure can be compressed using dictionary-based approaches and then directly queried without firstly decompressing the whole structure. Design/methodology/approach - The paper describes an architecture for partitioning XML into structural and dictionary elements and the subsequent manipulation of the dictionary elements to make the best use of available space. Findings - The results indicate that considerable savings are available by removing duplicate dictionaries. The paper also identifies the most effective strategy for defining dictionary scope. Research limitations/implications - This evaluation is based on a range of benchmark XML structures and the approach to minimising dictionary size shows benefit in the majority of these. Where structures are small and regular, the benefits of efficient dictionary representation are lost. The authors' future research now focuses on heuristics for further partitioning of structural elements. Practical implications - Mobile applications that need access to large data collections will benefit from the findings of this research. Traditional client/server architectures are not suited to dealing with high volume demands from a multitude of small mobile devices. Peer data sharing provides a more scalable solution and the experiments that the paper describes demonstrate the most effective way of sharing data in this context. Social implications - Many services are available via smartphone devices but users are wary of exploiting the full potential because of the need to conserve battery power. The approach mitigates this challenge and consequently expands the potential for users to benefit from mobile information systems. This will have impact in areas such as advertising, entertainment and education but will depend on the acceptability of file sharing being extended from the desktop to the mobile environment. Originality/value - The original work characterises the most effective way of sharing large data sets between small mobile devices. This will save battery power on devices such as smartphones, thus providing benefits to users of such devices

The use of alternative data models in data warehousing environments

Data Warehouses are increasing their data volume at an accelerated rate; high disk space consumption; slow query response time and complex database administration are common problems in these environments. The lack of a proper data model and an adequate architecture specifically targeted towards these environments are the root causes of these problems. Inefficient management of stored data includes duplicate values at column level and poor management of data sparsity which derives from a low data density, and affects the final size of Data Warehouses. It has been demonstrated that the Relational Model and Relational technology are not the best techniques for managing duplicates and data sparsity. The novelty of this research is to compare some data models considering their data density and their data sparsity management to optimise Data Warehouse environments. The Binary-Relational, the Associative/Triple Store and the Transrelational models have been investigated and based on the research results a novel Alternative Data Warehouse Reference architectural configuration has been defined. For the Transrelational model, no database implementation existed. Therefore it was necessary to develop an instantiation of it’s storage mechanism, and as far as could be determined this is the first public domain instantiation available of the storage mechanism for the Transrelational model

Workflow level interoperation of grid data resources

The lack of widely accepted standards and the use of different middleware solutions divide today’s Grid resources into non-interoperable production Grid islands. On the other hand, more and more experiments require such a large number of resources that the interoperation of existing production Grids becomes inevitable. This paper, based on the current results of grid interoperation studies, defines generic requirements towards the workflow level interoperation of grid solutions. It concentrates on intra-workflow interoperation of grid data resources, as one of the key areas of generic interoperation, and describes through an example how existing tools can be extended to achieve the required level of interoperation

Functional Dependencies in OWL ABox

Functional Dependency (FD) has been extensively studied in database theory. Most recently there have been some works investigating the implications of extending Description Logics with functional dependencies. In particular the OWL ontology language offers the functional property property allowing simple functional dependency to be specified. As it turns out, more complex FD specified as concept constructors has been proved to lead to undecidability in the general case, which restricts its usage as part of TBOX. This paper departs from previous ones by restricting FDs applicability to instances in the ABOX. We specify FD as a new constructor, an OWL concept. FD instances are mapped to Horn clauses and evaluated against the ABOX according to user’s desired behavior. The latter allows users to determine whether FDs should be interpreted as constraints, assertions or views. Our approach gives ontology users data guarantees usually found in databases, integrated with the ontology conceptual model

Functional dependencies in OWL ABoxes

Functional Dependency (FD) has been extensively studied in database theory. Most recently there have been some works investigating the implications of extending Description Logics with functional dependencies. In particular the OWL ontology language offers the functional property property allowing simple functional dependency to be specified. As it turns out, more complex FD specified as concept constructors has been proved to lead to undecidability in the general case, which restricts its usage as part of TBOX. This paper departs from previous ones by restricting FDs applicability to instances in the ABOX. We specify FD as a new constructor, an OWL concept. FD instances are mapped to Horn clauses and evaluated against the ABOX according to user’s desired behavior. The latter allows users to determine whether FDs should be interpreted as constraints, assertions or views. Our approach gives ontology users data guarantees usually found in databases, integrated with the ontology conceptual model

Der Lehrstuhl Datenbank- und Informationssysteme der Universität Rostock

Im Jahr 2014 feierte der Lehrstuhl Datenbank- und Informationssysteme (LS DBIS) an der Universität Rostock sein zwanzigjähriges Bestehen. Zur Jubiläumsveranstaltung mit ehemaligen und aktuellen Studenten, Mitarbeitern, Kollegen und Kooperationspartnern wurde diverses Material aus 20 Jahren aufbereitet. In diesem Beitrag soll daraus ein Rückblick auf 20 Jahre Forschung und Lehre im Bereich Datenbank- und Informationssysteme sowie ein Ein- und Ausblick auf aktuelle Forschungsarbeiten gegeben werden

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System