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

Knowledge Rich Natural Language Queries over Structured Biological Databases

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. In this paper, we propose a multi-level knowledge-based middleware to facilitate such mappings that separate the conceptual level from the physical level. We augment these multi-level abstractions with a concept reasoner and a query strategy engine to dynamically link arbitrary natural language querying to well defined structured queries. We demonstrate the feasibility of our approach by presenting a Datalog based prototype system, called BioSmart, that can compute responses to arbitrary natural language queries over arbitrary databases once a syntactic classification of the natural language query is made

Issues in the Design of a Pilot Concept-Based Query Interface for the Neuroinformatics Information Framework

This paper describes a pilot query interface that has been constructed to help us explore a "concept-based" approach for searching the Neuroscience Information Framework (NIF). The query interface is concept-based in the sense that the search terms submitted through the interface are selected from a standardized vocabulary of terms (concepts) that are structured in the form of an ontology. The NIF contains three primary resources: the NIF Resource Registry, the NIF Document Archive, and the NIF Database Mediator. These NIF resources are very different in their nature and therefore pose challenges when designing a single interface from which searches can be automatically launched against all three resources simultaneously. The paper first discusses briefly several background issues involving the use of standardized biomedical vocabularies in biomedical information retrieval, and then presents a detailed example that illustrates how the pilot concept-based query interface operates. The paper concludes by discussing certain lessons learned in the development of the current version of the interface

Semantic query languages for knowledge-based web services in a construction context

Since the early 2000s, different frameworks were set up to enable web-based collaboration in building projects. Unfortunately, none of these initiatives was granted a long life. Recently, however, the use of web technologies in the building industry has been gaining momentum again, considered some promising technologies for reaching a more interoperable BIM practice. Specifically, this relates to (1) Linked Data and Semantic Web technologies, and (2) cloud-based applications. In order to combine these into a network of interlinked applications and datastores, an agreed-upon mechanism for automatic communication and data retrieval needs to be used. Apart from the W3C standard SPARQL, often considered too high a threshold for developers to implement, there are some recent GraphQL-based solutions that simplify the querying process and its implementation into web services. In this paper, we review two recent open source technologies based on GraphQL, that enable to query Linked Data on the web: GraphQL-LD and HyperGraphQL

Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. The proposed X-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems, October 27, 201

Integration of Biological Sources: Exploring the Case of Protein Homology

Data integration is a key issue in the domain of bioin- formatics, which deals with huge amounts of heteroge- neous biological data that grows and changes rapidly. This paper serves as an introduction in the field of bioinformatics and the biological concepts it deals with, and an exploration of the integration problems a bioinformatics scientist faces. We examine ProGMap, an integrated protein homology system used by bioin- formatics scientists at Wageningen University, and several use cases related to protein homology. A key issue we identify is the huge manual effort required to unify source databases into a single resource. Un- certain databases are able to contain several possi- ble worlds, and it has been proposed that they can be used to significantly reduce initial integration efforts. We propose several directions for future work where uncertain databases can be applied to bioinformatics, with the goal of furthering the cause of bioinformatics integration

A platform for discovering and sharing confidential ballistic crime data.

Criminal investigations generate large volumes of complex data that detectives have to analyse and understand. This data tends to be "siloed" within individual jurisdictions and re-using it in other investigations can be difficult. Investigations into trans-national crimes are hampered by the problem of discovering relevant data held by agencies in other countries and of sharing those data. Gun-crimes are one major type of incident that showcases this: guns are easily moved across borders and used in multiple crimes but finding that a weapon was used elsewhere in Europe is difficult. In this paper we report on the Odyssey Project, an EU-funded initiative to mine, manipulate and share data about weapons and crimes. The project demonstrates the automatic combining of data from disparate repositories for cross-correlation and automated analysis. The data arrive from different cultural/domains with multiple reference models using real-time data feeds and historical databases

Bipolar querying of valid-time intervals subject to uncertainty

Databases model parts of reality by containing data representing properties of real-world objects or concepts. Often, some of these properties are time-related. Thus, databases often contain data representing time-related information. However, as they may be produced by humans, such data or information may contain imperfections like uncertainties. An important purpose of databases is to allow their data to be queried, to allow access to the information these data represent. Users may do this using queries, in which they describe their preferences concerning the data they are (not) interested in. Because users may have both positive and negative such preferences, they may want to query databases in a bipolar way. Such preferences may also have a temporal nature, but, traditionally, temporal query conditions are handled specifically. In this paper, a novel technique is presented to query a valid-time relation containing uncertain valid-time data in a bipolar way, which allows the query to have a single bipolar temporal query condition