Many scientific experiments produce large amounts of data using high-throughput devices. In order to analyse this type of data Knowledge Discovery systems are required. However, generic laboratory systems do not provide any contextual information about the system that is being studied. In these situations, Knowledge Discovery can be aided and validated by the use of Information integration tools. In this paper, we introduce InjbGrid, a data integration, middleware engine, designed to operate under a Grid framework. It focuses on providing information access services and offers all users a query system which is able to retain the familiarity with their spedtic sdentific applications while being diverse, flexible and open at the same time. The assumption there is that defining a common language for all queries is not desirable.

Abstract. Biomedical data are becoming increasingly complex and heterogeneous in nature. The data are stored in distributed information systems, using a variety of data models, and are processed by increasingly more complex tools that analyze and visualize them. We present in this paper our framework for integrating biomedical research data and tools into a unique Web front end. Our framework is applied to the University of Washington’s Human Brain Project. Specifically, we present solutions to four integration tasks: definition of complex mappings from relational sources to XML, distributed XQuery processing, generation of heterogeneous output formats, and the integration of heterogeneous data visualization and analysis tools. 1

Kleisli is a data transformation and integration system that can be used for any application where the data is typed, but has proven especially useful for bioinformatics applications. It extends the conventional flat relational data model supported by the query language SQL to a complex object data model supported by the collection programming language CPL. It also opens up the closed nature of commercial relational data management systems to an easily extensible system that performs complex transformations on autonomous data sources that are heterogeneous and geographically dispersed. This paper describes some implementation details and example applications of Kleisli. 1 Introduction The Kleisli system [14, 32, 33] is an advanced broad-scale integration technology that has proven very useful in the bioinformatics arena. Many bioinformatics problems require access to data sources that are large, highly heterogeneous and complex, constantly evolving, and geographically dispersed...

* Corresponding authors that have contributed equally to the work

The integration of database and programming languages is difficult due to the different data models and type systems prevalent in each field. We present a solution where the developer may express queries encompassing program and database data. The notation used for queries is based on comprehensions, a declarative style that does not impose any specific execution strategy. In our approach, the type safety of language-integrated queries is analyzed at compile-time, followed by a translation that optimizes for database evaluation. We show the translation total and semantics preserving, and introduce a language-independent classification. According to this classification, our approach compares favorably with Microsoft’s LINQ, today’s best known representative. We provide an implementation in terms of Scala compiler plugins, accepting two notations for queries: LINQ and the native Scala syntax for comprehensions. The prototype relies on Ferry, a query language that already supports comprehensions yet targets SQL:1999. The reported techniques pave the way for further progress in bridging the programming and the database worlds. 1

The extension of programming languages with database query capabilities is called language-integrated query. This is a desirable goal in connection with two recent developments (from the programming and the database communities): (a) the functional-object paradigm; and (b) enhanced expressiveness and conciseness of functional query languages. In addition, the extensible compiler architectures for modern programming languages along with the advanced optimization techniques for functional query languages bring new perspectives to persistent programming languages. The main results of the present master project work are contributions that fall under the larger ScalaQL project. The ScalaQL project proposes a translation algorithm from two source languages, LINQ and Scala, to SQL:1999 queries with the Ferry query language as an intermediate language. The underlying translations are required to be total and semantics preserving. The master work contributes to (a) providing the syntactic and semantic foundation for the second translation (from Scala into Ferry); (b) covering the relevant aspects of both the Scala and Ferry type systems; and (c) establishing the required isomorphism of types between the supported Scala subset and Ferry. Finally, the proposed approach has been implemented as a Scala compiler plugin that allows compile-time processing of LINQ queries and preparing well-formed SQL:1999 queries amenable to relational DBMS evaluation. Declaration I declare that: this work has been prepared by myself, all literal or content based quotations are clearly pointed out, and no other sources or aids than the declared ones have been used.