Design and implementation of queries for model-driven spreadsheets

This paper presents a domain-specific querying language for model-driven spreadsheets. We briefly show the design of the language and present in detail its implementation, from the denormalization of data and translation of our user-friendly query language to a more efficient query, to the execution of the query using Google. To validate our work, we executed an empirical study, comparing QuerySheet with an alternative spreadsheet querying tool, which produced positive results

Translating SQL to Spreadsheet: A Survey

Spreadsheets are the most popular and conventionally databases in use today. Since Spreadsheets are visual and expression based languages, research into the features of spreadsheets is therefore a highly relevant topic to study. Spreadsheet can be viewed as a Relation Database which contains a sheet and its corresponding information in terms of rows, while in RDBMS each table or say relation also represents its contained information in terms of rows. Each row represents a record which belongs to one or more relation. Spreadsheets uses different formulae to extract required information but it need expert knowledge about the tool and its usage. One can extend the usage of Spreadsheet in any direction as it provides great flexibility in terms of data storage and dependency of stored data. We surveyed some of research which took great attention over Spreadsheets and its applicability in different functional cases, such as Data Visualization, SQL Engines and many more. Our survey focuses on QUERYSHEET, ES-SQL, MDSHEET and PrediCalc [3], [5], [4], [8]. These different researches are motivations to our survey and attraction in Spreadsheets and its functional extensibility

A Framework for XML-based Integration of Data, Visualization and Analysis in a Biomedical Domain

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

Initial report on Object Spreadsheets

There is a growing demand for data-driven web applications that help automate organizational and business processes of low to medium complexity by letting users view and update structured data in controlled ways. We present Object Spreadsheets, an end-user development tool that combines a spreadsheet interface with a rich data model to help the process administrators build the logic for such applications themselves. Its all-in-one interface with immediate feedback has the potential to bring more complex tasks within reach of end-user developers, compared to existing approaches. Our data model is based on the structure of entity-relationship models and directly supports nested variable-size collections and object references, which are common in web applications but poorly accommodated by traditional spreadsheets. Object Spreadsheets has a formula language suited to the data model and supports stored procedures to specify the forms of updates that application users may make. Formulas can be used to assemble data in the exact structure in which it is to be shown in the application UI, simplifying the task of UI building; we intend for Object Spreadsheets to be integrated with a UI builder to provide a complete solution for application development. We describe our prototype implementation and several example applications we built to demonstrate the applicability of the tool

A Wizard for Composing SPARQL Queries in the GF Framework for Ontology-Based Data Access

Ontology-Based Data Access is a methodology concerned with bridging the gap between legacy data sources and semantic web technologies by providing protocols and tools for translating old data into ontologies. Querying modern ontologies represented as networks of objects interlinked by relations and properties and stored in OWL/RDF text files requires writing SPARQL queries, an activity requiring technical proficiency that is not usually in the hands of lay users. We extend our prototype of OBDA called GF to include the functionality of executing arbitrary SPARQL queries posed against OWL/RDF ontologies obtained by OBDA from H2 relational databases as well as Excel and CSV spreadsheets. To help naive users with less technical programming skills perform queries on such ontologies, we introduce a wizard for visually expressing a subset of SPARQL queries in a Query-By-Example approach.XV Workshop Innovación en sistemas de Software (WISS)Red de Universidades con Carreras en Informátic

Heterogeneous biomedical database integration using a hybrid strategy: a p53 cancer research database.

Complex problems in life science research give rise to multidisciplinary collaboration, and hence, to the need for heterogeneous database integration. The tumor suppressor p53 is mutated in close to 50% of human cancers, and a small drug-like molecule with the ability to restore native function to cancerous p53 mutants is a long-held medical goal of cancer treatment. The Cancer Research DataBase (CRDB) was designed in support of a project to find such small molecules. As a cancer informatics project, the CRDB involved small molecule data, computational docking results, functional assays, and protein structure data. As an example of the hybrid strategy for data integration, it combined the mediation and data warehousing approaches. This paper uses the CRDB to illustrate the hybrid strategy as a viable approach to heterogeneous data integration in biomedicine, and provides a design method for those considering similar systems. More efficient data sharing implies increased productivity, and, hopefully, improved chances of success in cancer research. (Code and database schemas are freely downloadable, http://www.igb.uci.edu/research/research.html.)