The number of content-generation devices is expected to increase explosively in the near future thanks to evolution in the field of input/output devices and Consumer Generated Media (CGM) tools. Users will simultaneou...
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ISBN:
(纸本)9781424436859
The number of content-generation devices is expected to increase explosively in the near future thanks to evolution in the field of input/output devices and Consumer Generated Media (CGM) tools. Users will simultaneously wear communicating sensor devices wherever they are, whenever they need them, and whatever they are doing. Under such circumstances, various contents will be generated, updated by the devices and moved much more frequently than they are now. We propose an architecture for a real-time information-delivery system that enables users to quickly discover the freshest, most accurate content. We compared three types of well-known data searching algorithms, i.e., in DNS-like static-tree method, Web crawler method, and Chord method, which can be used in servers in existing systems to retrieve the metadata on the content. We first developed a simulator and evaluated the algorithms to assess the methods to find which generation, update, and movement of content occurred more frequently. We discovered that the Web crawler method and Chord method both offered roughly the same correct answer rate. However, the number of messages increased dramatically with the Web crawler method when the content-update interval shortened. The average search time also increased with the Chord method as the number of user nodes increased.
This paper presents an automated, online approach to anomaly detection in high-content screening assays for pharmaceutical research. Online detection of anomalies is attractive because it offers the possibility of imm...
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ISBN:
(纸本)9781424420025
This paper presents an automated, online approach to anomaly detection in high-content screening assays for pharmaceutical research. Online detection of anomalies is attractive because it offers the possibility of immediate corrective action, early termination, and redesign of assays that may require many hours or days to execute. The proposed approach employs assay-specific image processing within an assay-independent framework for distributed control, machine learning, and anomaly reporting. Specifically, we exploit coarse-grained parallelism to distribute image processing over several computing nodes while efficiently aggregating sufficient statistics across nodes. This architecture also allows us to easily handle geographically-distributed data sources. Our results from two applications, adipocyte quantitation and neurite growth estimation, confirm that this online approach to anomaly detection is feasible, efficient, and accurate.
Peer selection for query routing is a core task in peer-to-peer networks. Unstructured peer-to-peer systems (like Gnutella) ignore this problem, leading to an abundance of network traffic. Structured peer-to-peer syst...
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Peer selection for query routing is a core task in peer-to-peer networks. Unstructured peer-to-peer systems (like Gnutella) ignore this problem, leading to an abundance of network traffic. Structured peer-to-peer systems (like Chord) enforce a particular, global way of distributing data among the peers in order to solve this problem, but then encounter problems of network volatility and conflicts with the autonomy of the peer data management. In this paper, we propose a new mechanism, INGA, which is based on the observation that query routing in social networks is made possible by locally available knowledge about the expertise of neighbors and a semantics-based peer selection function. We validate INGA by simulation experiments with different data sets. We compare INGA with competing peer selection mechanisms on resulting parameters like recall, message gain or number of messages produced.
Information retrieval techniques have to face both the growing amount of data to be processed and the "natural" distribution of these data over the network. Hence, we introduce in this paper a new architectu...
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ISBN:
(纸本)9781424404810
Information retrieval techniques have to face both the growing amount of data to be processed and the "natural" distribution of these data over the network. Hence, we introduce in this paper a new architecture for image retrieval in distributed image databases, based on multi-agent systems. Our system, inspired by "ant-agents", uses labels provided by the user for learning both the searched category of images and the path to the most relevant databases. We then show how effective can be our architecture on a generalist image database network.
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