Abstract: This paper proposes a query abstraction mechanism which allows web/mobile-service administrator to formulate a skeleton of a sequence of SQL queries by parameterizing holes, later being filled by end-users. The mechanism is generic as the administrator can use it to register multiple services, and expandable as the existing service can be specialized, to automatically generate new kinds of personalized services. An end-user’s input, when given in its entirety, initiates the automatic generation of appropriate SQL queries suitable for the user’s requested service. A personalized service can be devised by designating the end-user’s input parameters into static or dynamic. When static input arguments are given, a specialized skeleton service with respect to the given input is created. The mechanism is implemented to be used in systems for web/mobile-based information and transaction services.
Abstract: The amount of bio-medical data available over theWeb grows
exponentially with time. The large volume of the currently available data
makes it dicult to explore, while the velocity at which this data changes
and the variety of formats in which bio-medical is published makes it
dicult to access them in an integrated form. Moreover, the lack of an
integrated vocabulary makes querying this data dicult. In this paper,
we advocate the use of Linked Data to integrate, query and visualize big
bio-medical data. As a proof of concept, we show how the constant
flow of bio-medical publications can be integrated with the 7.36 billion large
Linked Cancer Genome Atlas dataset (TCGA). Then, we show how we
can harness the value hidden in that data by making it easy to explore
within a browsing interface. We evaluate the scalability of our approach
by comparing the query execution time of our system with that of FedX
on Linked TCGA.
Abstract: The Cancer Genome Atlas (TCGA) is a multidisciplinary, multi-institutional pilot project to create an atlas of genetic mutations responsible for cancer. One of the aims of this project is to develop an infrastructure for making the cancer related data publicly accessible, to enable cancer researchers anywhere around the world to make and validate important discoveries. However, data in the cancer genome atlas are organized as text archives in a set of directories. Devising bioinformatics applications to analyse such data is still challenging, as it requires downloading very large archives and parsing the relevant text files in order to collect the critical co-variates necessary for analysis. Furthermore, the various types of experimental results are not connected biologically, i.e. in order to truly exploit the data in the genome-wide context in which the TCGA project was devised, the data needs to be converted into a structured representation and made publicly available for remote querying and virtual integration. In this work, we address these issues by RDFizing data from TCGA and linking its elements to the Linked Open Data (LOD) Cloud. The outcome is the largest LOD data source (to the best of our knowledge) comprising of over 30 billion triples. This data source can be exploited through publicly available SPARQL endpoints, thus providing an easy-to-use, time-efficient, and scalable solution to accessing the Cancer Genome Atlas. We also describe showcases which are enabled by the new linked data representation of the Cancer Genome Atlas presented in this paper.
Abstract: Over the last years the Web of Data has developed into a large compendium of interlinked data sets from multiple domains. Due to the decentralized architecture of this compendium, several of these datasets contain duplicated data. Yet, so far, only little attention has been paid to the e ect of duplicated data on federated querying. This work presents DAW, a novel duplicate-aware approach to federated querying over the Web of Data. DAW is based on a combination of min-wise independent permutations and compact data summaries. It can be directly combined with existing federated query engines in order to achieve the same query recall values while querying fewer data sources. We extend three well-known federated query processing engines - DARQ, SPLENDID, and FedX - with DAW and compare our extensions with the original approaches. The comparison shows that DAW can greatly reduce the number of queries sent to the endpoints, while keeping high query recall values. Therefore, it can signi cantly improve the performance of federated query processing engines. Moreover, DAW provides a source selection mechanism that maximises the query
Abstract: The amount of bio-medical data available over theWeb grows exponentially with time. The large volume of the currently available data makes it dicult to explore, while the velocity at which this data changes and the variety of formats in which bio-medical is published makes it dicult to access them in an integrated form. Moreover, the lack of an integrated vocabulary makes querying this data dicult. In this paper, we advocate the use of Linked Data to integrate, query and visualize big bio-medical data. As a proof of concept, we show how the constant flow of bio-medical publications can be integrated with the 7.36 billion large Linked Cancer Genome Atlas dataset (TCGA). Then, we show how we can harness the value hidden in that data by making it easy to explore within a browsing interface. We evaluate the scalability of our approach by comparing the query execution time of our system with that of FedX on Linked TCGA.
