Tool for SPARQL Querying over Compact RDF Representations

We present an architecture for the efficient storing and querying of large RDF datasets. Our approach seeks to store RDF datasets in very little space while offering complete SPARQL functionality. To achieve this, our proposal was built over HDT, an RDF serialization framework, and its interaction with the Jena query engine. We propose a set of modifications to this framework in order to incorporate a range of space-efficient compact data structures for data storage and access, while using high-level capabilities to answer more complicated SPARQL queries. As a result, our approach provides a standard mechanism for using low-level data structures in complicated query situations requiring SPARQL searches, which are typically not supported by current solutions.

SmarT: Machine Learning Approach for Efficient Filtering and Retrieval of Spatial and Temporal Data in Big Data

Spatiotemporal data has always been big data. In these days, big data analytics for spatiotemporal data is receiving considerable attention to allow users to analyze huge amounts of data. Traditional big data platforms cannot handle all the challenges of processing spatio-temporal data. Although some big data platforms have been proposed to process a massive volume of spatiotemporal data, neither is considered a clear winner for all possible scenarios. This paper presents the SmarT query engine, a machine learning-based solution that chooses the best big data platform for processing spatiotemporal queries on the fly. In a detailed experimental evaluation, considering the Apache Spark, Elasticsearch, and SciDB big data platforms, the response time decreased up to 22% when using SmarT.

Language-agnostic integrated queries in a managed polyglot runtime

Language-integrated query (LINQ) frameworks offer a convenient programming abstraction for processing in-memory collections of data, allowing developers to concisely express declarative queries using general-purpose programming languages. Existing LINQ frameworks rely on the well-defined type system of statically-typed languages such as C # or Java to perform query compilation and execution. As a consequence of this design, they do not support dynamic languages such as Python, R, or JavaScript. Such languages are however very popular among data scientists, who would certainly benefit from LINQ frameworks in data analytics applications. In this work we bridge the gap between dynamic languages and LINQ frameworks. We introduce DynQ, a novel query engine designed for dynamic languages. DynQ is language-agnostic, since it is able to execute SQL queries in a polyglot language runtime. Moreover, DynQ can execute queries combining data from multiple sources, namely in-memory object collections as well as on-file data and external database systems. Our evaluation of DynQ shows performance comparable with equivalent hand-optimized code, and in line with common data-processing libraries and embedded databases, making DynQ an appealing query engine for standalone analytics applications and for data-intensive server-side workloads.

Charting the design space of query execution using VOILA

Database architecture, while having been studied for four decades now, has delivered only a few designs with well-understood properties. These few are followed by most actual systems. Acquiring more knowledge about the design space is a very time-consuming processes that requires manually crafting prototypes with a low chance of generating material insight. We propose a framework that aims to accelerate this exploration process significantly. Our framework enables synthesizing many different engines from a description in a carefully designed domain-specific language (VOILA). We explain basic concepts and formally define the semantics of VOILA. We demonstrate VOILA's flexibility by presenting translation back-ends that allow the synthesis of state-of-the-art paradigms (data-centric compilation, vectorized execution, AVX-512), mutations and mixes thereof. We show-case VOILA's flexibility by exploring the query engine design space in an automated fashion. We generated thousands of query engines and report our findings. Queries generated by VOILA achieve similar performance as state-of-the-art hand-optimized implementations and are up to 35.5X faster than well-known systems.

SUMA: A Partial Materialization-Based Scalable Query Answering in OWL 2 DL

Ontology-mediated querying (OMQ) provides a paradigm for query answering according to which users not only query records at the database but also query implicit information inferred from ontology. A key challenge in OMQ is that the implicit information may be infinite, which cannot be stored at the database and queried by off -the -shelf query engine. The commonly adopted technique to deal with infinite entailments is query rewriting, which, however, comes at the cost of query rewriting at runtime. In this work, the partial materialization method is proposed to ensure that the extension is always finite. The partial materialization technology does not rewrite query but instead computes partial consequences entailed by ontology before the online query. Besides, a query analysis algorithm is designed to ensure the completeness of querying rooted and Boolean conjunctive queries over partial materialization. We also soundly and incompletely expand our method to support highly expressive ontology language, OWL 2 DL. Finally, we further optimize the materialization efficiency by role rewriting algorithm and implement our approach as a prototype system SUMA by integrating off-the-shelf efficient SPARQL query engine. The experiments show that SUMA is complete on each test ontology and each test query, which is the same as Pellet and outperforms PAGOdA. Besides, SUMA is highly scalable on large datasets.

GIS Based Health Information Management through LETL, Multi Criteria Query, Analysis, Visualization

For effective decision making in public health information management(HIM) system, health information availability, accessibility, prompt exchange, GIS linkage, spatiotemporal analysis of diseases is crucial. Lack of cost-effective technical support and information gaps are the main obstacles in HIM. This article defines a generic conceptual process framework for effective HIM that provides cost-effective, portable, easy to use solution. The solution incorporates GIS, Mobile technology, information management concepts, ICD-10 codes, WHO and mHealth standards. The current research is implemented as an android application that facilitates: 1) Patient disease data collection, geospatial mapping of disease data and accumulate a centralized server 2) LETL that supports bulk disease data upload 3) Addresses syntactic and semantic heterogeneity in health data 4) A strong multi-criteria query engine, visualization and spatiotemporal analysis of diseases are designed with a global perspective to be used across the globe.

Retrieving Semantic Image Using Shape Descriptors and Latent-Dynamic Conditional Random Fields

This paper introduces a new approach to semantic image retrieval using shape descriptors as dispersion and moment in conjunction with discriminative classifier model of latent-dynamic conditional random fields (LDCRFs). The target region is firstly localized via the background subtraction model. Then the features of dispersion and moments are employed to k-means clustering to extract object’s feature as second stage. After that, the learning process is carried out by LDCRFs. Finally, simple protocol and RDF (resource description framework) query language (i.e. SPARQL) on input text or image query is to retrieve semantic image based on sequential processes of query engine, matching module and ontology manager. Experimental findings show that our approach can be successful to retrieve images against the mammal’s benchmark with retrieving rate of 98.11%. Such outcomes are likely to compare very positively with those accessible in the literature from other researchers.