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Problem Identification by Mining Trouble Tickets

2014
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International Conference on Management of Data
*

= Mode of the position of the word w * Label of the clique = Arrangment of the words in Cw according to

dblp:conf/comad/ShimpiNSK14
fatcat:b56slvpiofdrffjzcvmdp5cfom
*p*Figure 2 : 5 . 25 Figure 2: System-generated tickets: group id vs size of each group Figure ... a label • Identify set of common words Cw from the set of cleaned description belonging to the clique -For each word w in Cw, * Compute its position in set of cleaned description belonging to clique,*p*...##
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Sensor Selection Heuristic in Sensor Networks
[chapter]

2005
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Lecture Notes in Computer Science
*

We consider the problem of estimating the location of a moving target in a 2-D plane. In this paper, we focus attention on selecting an appropriate 3 rd sensor, given two sensors, with a view to minimize the estimation error. Only the selected sensors need to measure distance to the target and communicate the same to the central "tracker". This minimizes bandwidth and energy consumed in measurement and communication while achieving near minimum estimation error. In this paper, we have proposed

doi:10.1007/11602569_23
fatcat:63k6vlf3w5fqpdp6fygsy7cnda
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... hat the 3 rd sensor be selected based on three measures viz. (a) collinearity, (b) deviation from the ideal direction in which the sensor should be selected, and (c) proximity of the sensor from the target. We assume that the measurements are subject to multiplicative error. Further, we use least square error estimation technique to estimate the target location. Simulation results show that using the proposed algorithm it is possible to achieve near minimum error in target location. the central device responsible for estimating the location of the target, (b) the clocks of the sensors are synchronized so that the sensors "measure" distance at approximately the same time, and (c) the sensors are able to communicate their measurements to this central device, also referred to as the "tracker". Since the target is moving and since a sensor must be within a certain distance from the target (before it can detect the presence of the target and measure distance), we assume that there are several sensors, {s i } = Σ, spread across the 2-D plane. In fact, we assume that there are three or more sensors located in and around every point in the 2-D plane so that we can compute an estimate based on measurements from a subset of three sensors suitably selected to minimize estimation error. This approach also allows one to minimize communication overheads and conserve battery power available to sensors. Further, since the target is moving, the collection of sensors changes every time an estimate is required to be obtained. Specifically, we assume that as the target moves, if sensors {s 1 , s 2 , s 3 } have made measurements at time t k , then at time t k+1 , we drop one of the sensors s 1 , s 2 , or s 3 and select a sensor s 4 suitably so as to minimize the error in estimated location of the target. Accordingly, this paper is about suitably selecting the 3 rd sensor from a set of N k+1 sensors.##
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Analyzing Periodically Occurring Patterns in Time Series

2010
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International Conference on Management of Data
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Thus, ∀(mi, mj ) ∈ LM such that T ime(mi) < T ime(mj ): pair (mi, mj ) ∈ LM

dblp:conf/comad/SahaiNS10
fatcat:kqvpdyd2ejbdfmvpkolyaihcza
*P*if, (*p*− δ) ≤ (T ime(mj ) − T ime(mi) ≤ (*p*+ δ) This ensures that ∀(m i , m j ) ∈ LM*P*, the pattern T*p*will be bound by minima ... . • Time-series region: A time-series region T*p*of length*p*is a subsequence of*p*contiguous points in the timeseries. ...##
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Random and Periodic sleep schedules for target detection in sensor networks

2007
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2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems
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Specifically, we analyse and obtain for the Random wake-up schedule the expected delay in detection, and the delay, such that with probability

doi:10.1109/mobhoc.2007.4428677
dblp:conf/mass/SadaphalJ07
fatcat:sk7zscf4wzdezm3o556vsjwl4a
*P*, the delay is less than the computed value. ... As a result ∃*p*a q a n =*p*a m + a. For sure*p*a ≥ 0. ... Then, ε = δ s − δ t = 1E(∆) vs. δ s . and*p*1 = 0.1037,*p*= 0.1,*p*2 = 0.0906,*p*3 = 0.08, etc. ...##
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Table of Contents PROYEVA: System to Evaluate the Projects Quality in Contests Community-Commerce Brokering Arena for Opportunistic Cloud Services Offerings An Approach to Find Integration and Monitoring Points for Container Logistics Business Processes A Monitoring Approach for Dynamic Service-Oriented Architecture Systems

unpublished

Lopez-Soler Workload Characterization for Stability-As-A-Service

fatcat:c4hyytp2trcatmxyd7agenuqeu
*Vaishali**P*. ...*Sadaphal*and Maitreya Natu 84 4R of Service Innovation: Research, Requirements, Reliability and Responsibility Anastasiya Yurchyshyna, Abdelaziz Khadraoui, and Michel Leonard Visualization Method Based ...##
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Analytics-Based Solutions for Improving Alert Management Service for Enterprise Systems

2013
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2013 IEEE 13th International Conference on Data Mining Workshops
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Sunday and Monday. 2)

doi:10.1109/icdmw.2013.166
dblp:conf/icdm/KelkarNSSNS13
fatcat:twnr3okqjzemlodfrgkskcqmwi
*p*(dow=Sunday) = 1/16 = 0.06; 3)*p*(dow=M onday) = 15/16 = 0.93; 4) We next compute the entropy value for the day-of-week dimension: H dow = i∈Sunday,M onday −*p*i * log(*p*i ). 5) ... Given a set with n possible values {x 1 , x 2 , . . . x n }, the entropy is defined as H = n i −*p*i * log(*p*i ), where*p*i is the probability of occurrence of the value x i . ...##
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Varanus: More-with-less fault localization in data centers

2012
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2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012)
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Formally, information entropy H m (T ) for each candidate monitor node m ∈ T is defined as: H m (T ) = −

doi:10.1109/comsnets.2012.6151303
dblp:conf/comsnets/SadaphalNVS12
fatcat:qkdu4ditkrbz3dplmqbpssccea
*p*(T R m )log(*p*(T R m )) −*p*(T U m )log(*p*(T U m )) where*p*(T R m ) and*p*(T U m ) , respectively, denote ... The*p*-value thus obtained represents the expected amount of similarity between two current observation windows. We use this*p*-value as the similarity threshold. ...##
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Predico: A System for What-if Analysis in Complex Data Center Applications
[chapter]

2011
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Lecture Notes in Computer Science
*

We model each node as a M/G/1/

doi:10.1007/978-3-642-25821-3_7
fatcat:vvfdhrnhereq7o6tgzfdxnw5pq
*P*S queue i.e. the service times are assumed to have an arbitrary distribution and the service discipline at each node is assumed to be processor sharing (PS). ...##
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Analytical modeling for what-if analysis in complex cloud computing applications

2013
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Performance Evaluation Review
*

We model each node as a M/G/1/

doi:10.1145/2479942.2479949
fatcat:kmazmz6farasheawfgcw7ln3x4
*P*S queue i.e. the service times are assumed to have an arbitrary distribution and the service discipline at each node is assumed to be processor sharing (PS). ...##
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Workload Characterization for Stability-As-A-Service

unpublished

Then the

fatcat:3lriaz7jhneuvdrcphbgdftbcm
*p*-prediction heuristic states that if (*p*12 < M p ) then p 3 > min(*p*1 ,*p*2 ), where M*p*is the threshold defined for the*p*-prediction heuristic. ... Let the*p*-values of the t-test ran on performance data of these subsets and that of the rest of data are*p*1 and*p*2 respectively. ...