EAGLE—A Scalable Query Processing Engine for Linked Sensor Data

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE

Mejorando la Ciencia Abierta Usando Datos Abiertos Enlazados: Caso de Uso CONICET Digital

Los servicios de publicación científica están cambiando drásticamente, los investigadores demandan servicios de búsqueda inteligentes para descubrir y relacionar publicaciones científicas. Los editores deben incorporar información semántica para organizar mejor sus activos digitales y hacer que las publicaciones sean más visibles. En este documento, presentamos el trabajo en curso para publicar un subconjunto de publicaciones científicas de CONICET Digital como datos abiertos enlazados. El objetivo de este trabajo es mejorar la recuperación y la reutilización de datos a través de tecnologías de Web Semántica y Datos Enlazados en el dominio de las publicaciones científicas. Para lograr estos objetivos, se han tenido en cuenta los estándares de la Web Semántica y los esquemas RDF (Dublín Core, FOAF, VoID, etc.). El proceso de conversión y publicación se basa en las pautas metodológicas para publicar datos vinculados de gobierno. También describimos como estos datos se pueden vincular a otros conjuntos de datos como DBLP, Wikidata y DBPedia. Finalmente, mostramos algunos ejemplos de consultas que responden a preguntas que inicialmente no permite CONICET Digital.Scientific publication services are changing drastically, researchers demand intelligent search services to discover and relate scientific publications. Publishersneed to incorporate semantic information to better organize their digital assets and make publications more discoverable. In this paper, we present the on-going work to publish a subset of scientific publications of CONICET Digital as Linked Open Data. The objective of this work is to improve the recovery andreuse of data through Semantic Web technologies and Linked Data in the domain of scientific publications.To achieve these goals, Semantic Web standards and reference RDF schema?s have been taken into account (Dublin Core, FOAF, VoID, etc.). The conversion and publication process is guided by the methodological guidelines for publishing government linked data. We also outline how these data can be linked to other datasets DBLP, WIKIDATA and DBPEDIA on the web of data. Finally, we show some examples of queries that answer questions that initially CONICET Digital does not allowFil: Zárate, Marcos Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; ArgentinaFil: Carlos Buckle. Universidad Nacional de la Patagonia "San Juan Bosco"; ArgentinaFil: Mazzanti, Renato. Universidad Nacional de la Patagonia "San Juan Bosco"; ArgentinaFil: Samec, Gustavo Daniel. Universidad Nacional de la Patagonia "San Juan Bosco"; Argentin

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

GraphArchive - An Online Graph Data Store

In this report, we present our approach 'GraphArchive'. The solution attempts to enable researchers to exchange and archive graphs. The software is developed as an online platform using modern web technologies. In the document, features and architecture of GraphArchive are presented and the former approach 'GraphDB' is compared to the new system. Also, reader are taken on a typical walk through the system using a common use case for GraphArchive. News and development status of the system can be also visited at http://www.graph-archive.org

Creating an Intelligent System for Bankruptcy Detection: Semantic data Analysis Integrating Graph Database and Financial Ontology

In this paper, we propose a novel intelligent methodology to construct a Bankruptcy Prediction Computation Model, which is aimed to execute a company’s financial status analysis accurately. Based on the semantic data analysis and management, our methodology considers the Semantic Database System as the core of the system. It comprises three layers: an Ontology of Bankruptcy Prediction, Semantic Search Engine, and a Semantic Analysis Graph Database

The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing: Extended Survey

Graph processing is becoming increasingly prevalent across many application domains. In spite of this prevalence, there is little research about how graphs are actually used in practice. We performed an extensive study that consisted of an online survey of 89 users, a review of the mailing lists, source repositories, and whitepapers of a large suite of graph software products, and in-person interviews with 6 users and 2 developers of these products. Our online survey aimed at understanding: (i) the types of graphs users have; (ii) the graph computations users run; (iii) the types of graph software users use; and (iv) the major challenges users face when processing their graphs. We describe the participants' responses to our questions highlighting common patterns and challenges. Based on our interviews and survey of the rest of our sources, we were able to answer some new questions that were raised by participants' responses to our online survey and understand the specific applications that use graph data and software. Our study revealed surprising facts about graph processing in practice. In particular, real-world graphs represent a very diverse range of entities and are often very large, scalability and visualization are undeniably the most pressing challenges faced by participants, and data integration, recommendations, and fraud detection are very popular applications supported by existing graph software. We hope these findings can guide future research

Recent Developments in OCL and Textual Modelling

The panel session of the 16th OCL workshop featured a lightning talk session for discussing recent developments and open questions in the area of OCL and textual modelling. During this session, the OCL community discussed, stimulated through short presentations by OCL experts, tool support, potential future extensions, and suggested initiatives to make the textual modelling community even more successful. This collaborative paper, to which each OCL expert contributed one section, summarises the discussions as well as describes the recent developments and open questions presented in the lightning talks

Building a Knowledge Graph for the Air Traffic Management Community

Historically, most of the focus in the knowledge graph community has been on the support for web, social network, or product search applications. This paper describes some of our experience in developing a large-scale applied knowledge graph for a more technical audience with more specialized information access and analysis needs - the air traffic management community. We describe ATMGRAPH (NASA's Air Traffic Management (ATM) Knowledge Graph), a knowledge graph created by integrating various sources of structured aviation data, provided in large part by US federal agencies. We review some of the practical challenges we faced in creating this knowledge graph

NoSQL: Latar Belakang, Konsep, dan Kritik

Berkembangnya aplikasi berbasis web yang memerlukan pengolahan data dalam skala besar melahirkan paradigma baru dalam teknologi basis data. Beberapa website seperti Facebook, Twitter, Digg, Google, Amazon, dan SourceForge menyimpan dan mengolah data puluhan giga setiap harinya, dan total keseluruhan data yang disimpan oleh applikasi tersebut sudah mencapai ukuran petabyte. Ukuran data yang sangat besar menimbulkan permasalahan dari segi skalabilitas, karena pertambahan data yang terjadi setiap saat. Peningkatan kemampuan server secara vertikal yang dimiliki Relational Database Management System (RDBMS) terbatas pada penambahan prosesor, memori, dan media penyimpanan dalam satu node server yang terbatas. Sedangkan peningkatan kemampuan server secara horizontal yang meliputi penambahan perangkat server baru dalam suatu jaringan memerlukan biaya yang mahal dan sulit dalam pengelolaannya. Salah satu cara yang diterapkan oleh website berskala besar untuk mengatasi permasalahan tersebut adalah dengan menggunakan NoSQL, sebuah paradigma basis data yang merelaksasikan aturan-aturan konsistensi yang terdapat pada basis data relasional. Jika RDBMS menggunakan aturan Atomicity, Consistency, Isolation, dan Durability (ACID) untuk penyimpanan dan pengolahan data, maka NoSQL menggunakan paradigma Basically Available, Soft State, and Eventually consistent (BASE) untuk merelaksasikan aturan tersebut. Hasilnya, NoSQL dapat mengolah data dalam jumlah besar dengan memartisi data ke dalam beberapa server secara lebih mudah. Makalah ini membahas dan menjelaskan latar belakang kemunculan, konsep dasar, dan penggunaan NoSQL. Kata kunci : Basis data, RDBMS, Skalabilitas, NoSQ