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The rise of diversified data engines has created the need for federated queries. A federated query can take a query and provide data analysis based on data from various data engines. Since the query data originates from multiple data engines, federated queries usually rely on join operation and data migration to complete the query and take a long time. The challenges of optimizing federated queries lie on join order selection and data migration coordination. However, enumerating all join orders is impractical because the set of join orders grows exponentially with the number of relations to be joined. To improve the performance of federated queries, we present a deep reinforcement learning-based approach on optimizing join order and join engine selection for federated queries and design an deep Q-network-based (DQN-based) optimizer. The DQN-based optimizer can generate join search policies that optimize the join order selection for datasets with a given cost model. Based on the DQN-based optimizer, we implement a federated query system Coral which can provide optimization for join order selection of federated queries. With the optimized join order, Coral can transform a federated query into a set of subqueries which will be assigned to and executed on different data engines. We also propose a subquery cache optimization to optimize data migration during the query execution. The extensive experimental evaluation demonstrates that Coral can significantly reduce the query latency of federated queries and achieve a speedup of up to 5.03 × compared to the cutting-edge federated query systems. [ABSTRACT FROM AUTHOR]

In recent years, educational institutions have worked hard to automate their work using more trending technologies that prove the success in supporting decision-making processes. Most of the decisions in educational institutions rely on rating the academic research profiles of their staff. An enormous amount of scholarly data is produced continuously by online libraries that contain data about publications, citations, and research activities. This kind of data can change the accuracy of the academic decisions, if linked with the local data of universities. The linked data technique in this study is applied to generate a link between university semantic data and a scientific knowledge graph, to enrich the local data and improve academic decisions. As a proof of concept, a case study was conducted to allocate the best academic staff to teach a course regarding their profile, including research records. Further, the resulting data are available to be reused in the future for different purposes in the academic domain. Finally, we compared the results of this link with previous work, as evidence of the accuracy of leveraging this technology to improve decisions within universities. [ABSTRACT FROM AUTHOR]

To satisfy a user’s complex requirements, Resource Description Framework (RDF) Stream Processing (RSP) systems envision the fusion of remote RDF data with semantic streams, using common data models to query semantic streams continuously. While streaming data are changing at a high rate and are pushed into RSP systems, the remote RDF data are retrieved from different remote sources. With the growth of SPARQL endpoints that provide access to remote RDF data, RSP systems can easily integrate the remote data with streams. Such integration provides new opportunities for mixing static (or quasi-static) data with streams on a large scale. However, the current RSP systems do not offer any optimisation for the integration. In this article, we present an adaptive plan-based approach to efficiently integrate sematic streams with the static data from a remote source. We create a query execution plan based on temporal constraints among constituent services for the timely acquisition of remote data. To predict the change of remote sources in real time, we propose an adaptive process of detecting a source update, forecasting the update in the future, deciding a new plan to obtain remote data and reacting to a new plan. We extend a SPARQL query with operators for describing the multiple strategies of the proposed adaptive process. Experimental results show that our approach is more efficient than the conventional RSP systems in distributed settings. [ABSTRACT FROM AUTHOR]

Cross-institutional data sharing for cohort discovery is critical to enabling future research. While particularly useful in rare diseases, the ability to target enrollment and to determine if an institution has a sufficient number of patients is valuable in all research, particularly in the initiation of projects and collaborations. An optimal technology solution would work with any source database with minimal resource investment for deployment and would meet all necessary security and confidentiality requirements of participating organizations. We describe a platform-neutral reference implementation to meet these requirements: the Federated Aggregate Cohort Estimator (FACE). FACE was developed and implemented through a collaboration of The University of Alabama at Birmingham (UAB), The Ohio State University (OSU), the University of Massachusetts Medical School (UMMS), and the Denver Health and Hospital Authority (DHHA) a clinical affiliate of the Colorado Clinical and Translational Sciences Institute. The reference implementation of FACE federated diverse SQL data sources and an i2b2 instance to estimate combined research subject availability from three institutions. It used easily-deployed virtual machines and addressed privacy and security concerns for data sharing. [ABSTRACT FROM AUTHOR]

In view of the need for a highly distributed and federated architecture, a robust query expansion has great impact on the performance of information retrieval in a specific domain. We aim to determine ontology-driven query expansion terms using different weighting techniques to determine the most k-top relevant terms. For this, first we consider each individual ontology and user query keywords to determine the Basic Expansion Terms (BET). Second, we specify New Expansion Terms (NET) by Ontology Alignment (OA). Third, we use a Map-Reduce distributed algorithm for calculating all the shortest paths in ontology graph as a meta data to calculate weights for terms ∈ BET ∪ NET. Fourth, we actually weight expanded terms using a combination of semantic metrics namely Density Measure (DM), Betweenness Measure (BM), and Semantic Similarity Measure (SSM). Map/Reduce algorithm improves the efficiency of BET calculation especifically for BM and SSM calculation using the benefits of parallel processing. Finally, we use a Specific Interval(SI) to determine a set of Robust Expansion Terms (RET) and compare the result of our novel weighting approach with existing expansion approaches. We also show the effectiveness of our robust expansion in federated architecture. [ABSTRACT FROM AUTHOR]

The article discusses a software architecture capable of federating data from multiple heterogeneous health informatics data sources called Federated Utah Research & Translational Health e-Repository (FURTHeR). Topics covered include the software architecture comprising of federated query engine and data source facades, and the possibility to integrated FURTHeR with existing Service Oriented Architecture (SOA) Healthcare and Health Level 7 (HL7) frameworks.

Geospatial data is increasingly being made available on the Web as knowledge graphs using Linked Data principles. This entails adopting the best practices for publishing, retrieving, and using data, providing relevant initiatives that play a prominent role in the Web of Data. Despite the appropriate progress related to the amount of geospatial data available, knowledge graphs still face significant limitations in the GIScience community since their use, consumption, and exploitation are scarce, especially considering that just a few developments retrieve and consume geospatial knowledge graphs from within GIS. To overcome these limitations and address some critical challenges of GIScience, standards and specific best practices for publishing, retrieving, and using geospatial data on the Web have appeared. Nevertheless, there are few developments and experiences that support the possibility of expressing queries across diverse knowledge graphs to retrieve and process geospatial data from different and distributed sources. In this scenario, we present an approach to request, retrieve, and consume (geospatial) knowledge graphs available at diverse and distributed platforms, prototypically implemented on Apache Marmotta, supporting SPARQL 1.1 and GeoSPARQL standards. Moreover, our approach enables the consumption of geospatial knowledge graphs through a lightweight web application or QGIS. The potential of this work is shown with two examples that use GeoSPARQL-based knowledge graphs. [ABSTRACT FROM AUTHOR]