Dynamic Discovery of Type Classes and Relations in Semantic Web Data

The continuing development of Semantic Web technologies and the increasing user adoption in the recent years have accelerated the progress incorporating explicit semantics with data on the Web. With the rapidly growing RDF (Resource Description Framework) data on the Semantic Web, processing large semantic graph data have become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method

Controlling services in a mobile context-aware infrastructure

Context-aware application behaviors can be described as logic rules following the Event-Control-Action (ECA) pattern. In this pattern, an Event models an occurrence of interest (e.g., a change in context); Control specifies a condition that must hold prior to the execution of the action; and an Action represents the invocation of arbitrary services. We have defined a Controlling service aiming at facilitating the dynamic configuration of ECA rule specifications by means of a mobile rule engine and a mechanism that distributes context reasoning activities to a network of context processing nodes. In this paper we present a novel context modeling approach that provides application developers and users with more appropriate means to define context information and ECA rules. Our approach makes use of ontologies to model context information and has been developed on top of web services technology

Adverse selection costs, trading activity and price discovery in the NYSE: An empirical analysis

This paper studies the role that trading activity plays in the price discovery process of a NYSE-listed stock. We measure the expected information content of each trade by estimating its permanent price impact. It depends on observable trade features and market conditions. We also estimate the time required for quotes to incorporate all the information content of a particular trade. Our results show that price discovery is faster after risky trades and also at the extreme intervals of the session. The quote adjustment to trade-related shocks is progressive and this causes risk persistency and unusual short-term market conditions.Publicad

Discovery of the Spin Frequency of 4U 0614+09 with SWIFT/BAT

We report the discovery of burst oscillations at 414.7 Hz during a thermonuclear X-ray burst from the low mass X-ray binary (LMXB) 4U 0614+091 with the Burst Alert Telescope (BAT) onboard SWIFT. In a search of the BAT archive, we found two burst triggers consistent with the position of 4U 0614+091. We searched both bursts for high frequency timing signatures, and found a significant detection at 414.7 Hz during a 5 s interval in the cooling tail of the brighter burst. This result establishes the spin frequency of the neutron star in 4U 0614+091 as 415 Hz. The oscillation had an average amplitude (rms) of 14%, These results are consistent with those known for burst oscillations seen in other LMXBs. The inferred ratio of the frequency difference between the twin kHz QPOs, and the spin frequency in this source is strongly inconsistent with either 0.5 or 1, and tends to support the recent suggestions by Yin et al., and Mendez & Belloni, that the kHz QPO frequency difference may not have a strong connection to the neutron star spin frequency.Comment: 10 pages, 3 figures. AASTeX. Accepted for publication in the Astrophysical Journal Letter