A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

Bioinformatics service reconciliation by heterogeneous schema transformation

This paper focuses on the problem of bioinformatics service reconciliation in a generic and scalable manner so as to enhance interoperability in a highly evolving field. Using XML as a common representation format, but also supporting existing flat-file representation formats, we propose an approach for the scalable semi-automatic reconciliation of services, possibly invoked from within a scientific workflows tool. Service reconciliation may use the AutoMed heterogeneous data integration system as an intermediary service, or may use AutoMed to produce services that mediate between services. We discuss the application of our approach for the reconciliation of services in an example bioinformatics workflow. The main contribution of this research is an architecture for the scalable reconciliation of bioinformatics services

XML Matchers: approaches and challenges

Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure

Using Element Clustering to Increase the Efficiency of XML Schema Matching

Schema matching attempts to discover semantic mappings between elements of two schemas. Elements are cross compared using various heuristics (e.g., name, data-type, and structure similarity). Seen from a broader perspective, the schema matching problem is a combinatorial problem with an exponential complexity. This makes the naive matching algorithms for large schemas prohibitively inefficient. In this paper we propose a clustering based technique for improving the efficiency of large scale schema matching. The technique inserts clustering as an intermediate step into existing schema matching algorithms. Clustering partitions schemas and reduces the overall matching load, and creates a possibility to trade between the efficiency and effectiveness. The technique can be used in addition to other optimization techniques. In the paper we describe the technique, validate the performance of one implementation of the technique, and open directions for future research

Condor services for the Global Grid:interoperability between Condor and OGSA

In order for existing grid middleware to remain viable it is important to investigate their potentialfor integration with emerging grid standards and architectural schemes. The Open Grid ServicesArchitecture (OGSA), developed by the Globus Alliance and based on standard XML-based webservices technology, was the first attempt to identify the architectural components required tomigrate towards standardized global grid service delivery. This paper presents an investigation intothe integration of Condor, a widely adopted and sophisticated high-throughput computing softwarepackage, and OGSA; with the aim of bringing Condor in line with advances in Grid computing andprovide the Grid community with a mature suite of high-throughput computing job and resourcemanagement services. This report identifies mappings between elements of the OGSA and Condorinfrastructures, potential areas of conflict, and defines a set of complementary architectural optionsby which individual Condor services can be exposed as OGSA Grid services, in order to achieve aseamless integration of Condor resources in a standardized grid environment