11,521 research outputs found
Service discovery using Bloom filters
A protocol to perform service discovery in adhoc networks is introduced in this paper. Attenuated Bloom filters are used to distribute services to nodes in the neighborhood and thus enable local service discovery. The protocol has been implemented in a discrete event simulator to investigate the behavior in case of a multihop mobile ad-hoc network with nodes that all have services to offer. Methods to optimize the used bandwidth, which is a scarce resource in wireless networks, are investigated. Experiments performed with the simulator suggest that the proposed service discovery system enables users to find local services in a multihop ad-hoc network efficiently. The costs for advertising can be kept low, whereas the additional costs for queries set due to so-called false positives are moderate
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
A schema-based P2P network to enable publish-subscribe for multimedia content in open hypermedia systems
Open Hypermedia Systems (OHS) aim to provide efficient dissemination, adaptation and integration of hyperlinked multimedia resources. Content available in Peer-to-Peer (P2P) networks could add significant value to OHS provided that challenges for efficient discovery and prompt delivery of rich and up-to-date content are successfully addressed. This paper proposes an architecture that enables the operation of OHS over a P2P overlay network of OHS servers based on semantic annotation of (a) peer OHS servers and of (b) multimedia resources that can be obtained through the link services of the OHS. The architecture provides efficient resource discovery. Semantic query-based subscriptions over this P2P network can enable access to up-to-date content, while caching at certain peers enables prompt delivery of multimedia content. Advanced query resolution techniques are employed to match different parts of subscription queries (subqueries). These subscriptions can be shared among different interested peers, thus increasing the efficiency of multimedia content dissemination
On-Demand Big Data Integration: A Hybrid ETL Approach for Reproducible Scientific Research
Scientific research requires access, analysis, and sharing of data that is
distributed across various heterogeneous data sources at the scale of the
Internet. An eager ETL process constructs an integrated data repository as its
first step, integrating and loading data in its entirety from the data sources.
The bootstrapping of this process is not efficient for scientific research that
requires access to data from very large and typically numerous distributed data
sources. a lazy ETL process loads only the metadata, but still eagerly. Lazy
ETL is faster in bootstrapping. However, queries on the integrated data
repository of eager ETL perform faster, due to the availability of the entire
data beforehand.
In this paper, we propose a novel ETL approach for scientific data
integration, as a hybrid of eager and lazy ETL approaches, and applied both to
data as well as metadata. This way, Hybrid ETL supports incremental integration
and loading of metadata and data from the data sources. We incorporate a
human-in-the-loop approach, to enhance the hybrid ETL, with selective data
integration driven by the user queries and sharing of integrated data between
users. We implement our hybrid ETL approach in a prototype platform, Obidos,
and evaluate it in the context of data sharing for medical research. Obidos
outperforms both the eager ETL and lazy ETL approaches, for scientific research
data integration and sharing, through its selective loading of data and
metadata, while storing the integrated data in a scalable integrated data
repository.Comment: Pre-print Submitted to the DMAH Special Issue of the Springer DAPD
Journa
- …