28,982 research outputs found
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Architecture for Cooperative Prefetching in P2P Video-on- Demand System
Most P2P VoD schemes focused on service architectures and overlays
optimization without considering segments rarity and the performance of
prefetching strategies. As a result, they cannot better support VCRoriented
service in heterogeneous environment having clients using free VCR controls.
Despite the remarkable popularity in VoD systems, there exist no prior work
that studies the performance gap between different prefetching strategies. In
this paper, we analyze and understand the performance of different prefetching
strategies. Our analytical characterization brings us not only a better
understanding of several fundamental tradeoffs in prefetching strategies, but
also important insights on the design of P2P VoD system. On the basis of this
analysis, we finally proposed a cooperative prefetching strategy called
"cooching". In this strategy, the requested segments in VCR interactivities are
prefetched into session beforehand using the information collected through
gossips. We evaluate our strategy through extensive simulations. The results
indicate that the proposed strategy outperforms the existing prefetching
mechanisms.Comment: 13 Pages, IJCN
Heuristics Miners for Streaming Event Data
More and more business activities are performed using information systems.
These systems produce such huge amounts of event data that existing systems are
unable to store and process them. Moreover, few processes are in steady-state
and due to changing circumstances processes evolve and systems need to adapt
continuously. Since conventional process discovery algorithms have been defined
for batch processing, it is difficult to apply them in such evolving
environments. Existing algorithms cannot cope with streaming event data and
tend to generate unreliable and obsolete results.
In this paper, we discuss the peculiarities of dealing with streaming event
data in the context of process mining. Subsequently, we present a general
framework for defining process mining algorithms in settings where it is
impossible to store all events over an extended period or where processes
evolve while being analyzed. We show how the Heuristics Miner, one of the most
effective process discovery algorithms for practical applications, can be
modified using this framework. Different stream-aware versions of the
Heuristics Miner are defined and implemented in ProM. Moreover, experimental
results on artificial and real logs are reported
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