42,383 research outputs found
Getting Clusters from Structure Data and Attribute Data
International audienceIf the clustering task is widely studied both in graph clustering and in non supervised learning, combined clustering which exploits simultaneously the relationships between the ver- tices and attributes describing them, is quite new. In this paper, we present different scenarios for this task and, we evaluate their performances and their results on a dataset, with ground truth, built from several sources and containing a scientiïŹc social network in which textual data is associated to each vertex and the classes are known. We argue that, depending on the kind of data we have and the type of results we want, the choice of the clustering method is important and we present some concrete examples for underlining this
Energy efficient privacy preserved data gathering in wireless sensor networks having multiple sinks
Wireless sensor networks (WSNs) generally have a many-to-one structure so that event information flows from sensors to a unique sink. In recent WSN applications, many-tomany structures are evolved due to need for conveying collected event information to multiple sinks at the same time. This study proposes an anonymity method bases on k-anonymity for preventing record disclosure of collected event information in WSNs. Proposed method takes the anonymity requirements of multiple sinks into consideration by providing different levels of privacy for each destination sink. Attributes, which may identify of an event owner, are generalized or encrypted in order to
meet the different anonymity requirements of sinks. Privacy guaranteed event information can be multicasted to all sinks instead of sending to each sink one by one. Since minimization of energy consumption is an important design criteria for WSNs, our method enables us to multicast the same event information
to multiple sinks and reduce energy consumption
A Monitoring System for the BaBar INFN Computing Cluster
Monitoring large clusters is a challenging problem. It is necessary to
observe a large quantity of devices with a reasonably short delay between
consecutive observations. The set of monitored devices may include PCs, network
switches, tape libraries and other equipments. The monitoring activity should
not impact the performances of the system. In this paper we present PerfMC, a
monitoring system for large clusters. PerfMC is driven by an XML configuration
file, and uses the Simple Network Management Protocol (SNMP) for data
collection. SNMP is a standard protocol implemented by many networked
equipments, so the tool can be used to monitor a wide range of devices. System
administrators can display informations on the status of each device by
connecting to a WEB server embedded in PerfMC. The WEB server can produce
graphs showing the value of different monitored quantities as a function of
time; it can also produce arbitrary XML pages by applying XSL Transformations
to an internal XML representation of the cluster's status. XSL Transformations
may be used to produce HTML pages which can be displayed by ordinary WEB
browsers. PerfMC aims at being relatively easy to configure and operate, and
highly efficient. It is currently being used to monitor the Italian
Reprocessing farm for the BaBar experiment, which is made of about 200 dual-CPU
Linux machines.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics
(CHEP03), La Jolla, Ca, USA, March 2003, 10 pages, LaTeX, 4 eps figures. PSN
MOET00
Spatial clustering method for geographic data
In the process of visualizing quantitative spatial data, it is necessary to
classify attribute values into some class divisions. In a previous paper, the author
proposed a classification method for minimizing the loss of information contained in
original data. This method can be considered as a kind of smoothing method that
neglects the characteristics of spatial distribution. In order to understand the
spatial structure of data, it is also necessary to construct another smoothing method
considering the characteristics of the distribution of the spatial data. In this paper,
a spatial clustering method based on Akaikeâs Information Criterion is proposed.
Furthermore, numerical examples of its application are shown using actual spatial
data for the Tokyo Metropolitan area
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