Semantic Annotation and Reasoning for Sensor Data

Developments in (wireless) sensor and actuator networks and the capabilities to manufacture low cost and energy efficient networked embedded devices have lead to considerable interest in adding real world sense to the Internet and the Web. Recent work has raised the idea towards combining the Internet of Things (i.e. real world resources) with semantic Web technologies to design future service and applications for the Web. In this paper we focus on the current developments and discussions on designing Semantic Sensor Web, particularly, we advocate the idea of semantic annotation with the existing authoritative data published on the semantic Web. Through illustrative examples, we demonstrate how rule-based reasoning can be performed over the sensor observation and measurement data and linked data to derive additional or approximate knowledge. Furthermore, we discuss the association between sensor data, the semantic Web, and the social Web which enable construction of context-aware applications and services, and contribute to construction of a networked knowledge framework

An Approach to Construct Dynamic Service Mashups using Lightweight Semantics

Thousands of Web services have been available online, and mashups built upon them have been creating added value. However, mashups are mostly developed with a predefined set of services and components. The extensions to them always involve programming work. Furthermore, when a service is unavailable, it is challenging for mashups to smoothly switch to an alternative that others similar functionalities. To address these problems, this paper presents a novel approach to enable mashups to select and invoke semantic Web services on they. To extend a mashup with new semantic services, developers are only required to register and publish them as Linked Data. By refining the strategies of service selection, mashups can behave more adaptively and other higher fault-tolerance

SRBench: A streaming RDF/SPARQL benchmark

We introduce SRBench, a general-purpose benchmark primarily designed for streaming RDF/SPARQL engines, completely based on real-world data sets from the Linked Open Data cloud. With the increasing problem of too much streaming data but not enough tools to gain knowledge from them, researchers have set out for solutions in which Semantic Web technologies are adapted and extended for publishing, sharing, analysing and understanding streaming data. To help researchers and users comparing streaming RDF/SPARQL (strRS) engines in a standardised application scenario, we have designed SRBench, with which one can assess the abilities of a strRS engine to cope with a broad range of use cases typically encountered in real-world scenarios. The data sets used in the benchmark have been carefully chosen, such that they represent a realistic and relevant usage of streaming data. The benchmark defines a concise, yet omprehensive set of queries that cover the major aspects of strRS processing. Finally, our work is complemented with a functional evaluation on three representative strRS engines: SPARQLStream, C-SPARQL and CQELS. The presented results are meant to give a first baseline and illustrate the state-of-the-art

Access Control for Data Integration in Presence of Data Dependencies

International audienceDefining access control policies in a data integration scenario is a challenging task. In such a scenario typically each source specifies its local access control policy and cannot anticipate data inferences that can arise when data is integrated at the mediator level. Inferences, e.g., using functional dependencies, can allow malicious users to obtain, at the mediator level, prohibited information by linking multiple queries and thus violating the local policies. In this paper, we propose a framework, i.e., a methodology and a set of algorithms, to prevent such violations. First, we use a graph-based approach to identify sets of queries, called violating transactions, and then we propose an approach to forbid the execution of those transactions by identifying additional access control rules that should be added to the mediator. We also state the complexity of the algorithms and discuss a set of experiments we conducted by using both real and synthetic datasets. Tests also confirm the complexity and upper bounds in worst-case scenarios of the proposed algorithms

Formation and Evolution of Supermassive Black Holes

The correlation between the mass of supermassive black holes in galaxy nuclei and the mass of the galaxy spheroids or bulges (or more precisely their central velocity dispersion), suggests a common formation scenario for galaxies and their central black holes. The growth of bulges and black holes can commonly proceed through external gas accretion or hierarchical mergers, and are both related to starbursts. Internal dynamical processes control and regulate the rate of mass accretion. Self-regulation and feedback are the key of the correlation. It is possible that the growth of one component, either BH or bulge, takes over, breaking the correlation, as in Narrow Line Seyfert 1 objects. The formation of supermassive black holes can begin early in the universe, from the collapse of Population III, and then through gas accretion. The active black holes can then play a significant role in the re-ionization of the universe. The nuclear activity is now frequently invoked as a feedback to star formation in galaxies, and even more spectacularly in cooling flows. The growth of SMBH is certainly there self-regulated. SMBHs perturb their local environment, and the mergers of binary SMBHs help to heat and destroy central stellar cusps. The interpretation of the X-ray background yields important constraints on the history of AGN activity and obscuration, and the census of AGN at low and at high redshifts reveals the downsizing effect, already observed for star formation. History appears quite different for bright QSO and low-luminosity AGN: the first grow rapidly at high z, and their number density decreases then sharply, while the density of low-luminosity objects peaks more recently, and then decreases smoothly.Comment: 31 pages, 13 figures, review paper for Astrophysics Update

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Integration operators for generating RDF/OWL-based user defined mediator views in a grid environment

Research and development activities relating to the grid have generally focused on applications where data is stored in files. However, many scientific and commercial applications are highly dependent on Information Servers (ISs) for storage and organization of their data. A data-information system that supports operations on multiple information servers in a grid environment is referred to as an interoperable grid system. Different perceptions by end-users of interoperable systems in a grid environment may lead to different reasons for integrating data. Even the same user might want to integrate the same distributed data in various ways to suit different needs, roles or tasks. Therefore multiple mediator views are needed to support this diversity. This paper describes our approach to supporting semantic interoperability in a heterogeneous multi-information server grid environment. It is based on using Integration Operators for generating multiple semantically rich RDF/OWL-based user defined mediator views above the grid participating ISs. These views support different perceptions of the distributed and heterogeneous data available. A set of grid services are developed for the implementation of the mediator views

Geospatial Semantics: Why, of What, and How?

Abstract. Why are notions like semantics and ontologies suddenly getting so much attention, within and outside geospatial information communities? The main reason lies in the componentization of Geographic Information Systems (GIS) into services, which are supposed to interoperate within and across these communities. Consequently, I look at geospatial semantics in the context of semantic interoperability. The paper clarifies the relevant notion of semantics and shows what parts of geospatial information need to receive semantic speci-fications in order to achieve interoperability. No attempt at a survey of ap-proaches to provide semantics is made, but a framework for solving interopera-bility problems is proposed in the form of semantic reference systems. Particular emphasis is put on the need and possible ways to ground geospatial semantics in physical processes and measurements. 1. Introduction: Wh

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy