Analyzing Failures of a Semi-Structured Supercomputer Log File Efficiently by Using PIG on Hadoop

Size: px
Start display at page:

Download "Analyzing Failures of a Semi-Structured Supercomputer Log File Efficiently by Using PIG on Hadoop"

Transcription

1 Intrnational Journal of Computr Scinc and Enginring Opn Accss Rsarch Papr Volum-2, Issu-1 E-ISSN: Analyzing Failurs of a Smi-Structurd Suprcomputr Log Fil Efficintly by Using PIG on Hadoop Madhuri Srinivas Pall 1*, Konisa Jyothsna 2 & B. Anusha 3 1* Computr Scinc Enginring, National Institut of Tchnology, Warangal, India, madhuri.pall@yahoo.com 2 Computr Scinc Enginring, National Institut of Tchnology, Warangal, India, jyothsna1503@gmail.com 3 Computr Scinc Enginring, National Institut of Tchnology, Warangal, India, anushabattula321@gmail.com Rcivd: 12/12/2013 Rvisd: 20/12/2013 Accptd: 28/12/2013 Publishd: 31/Jan/2014 Abstract Data sts usd to ful th rcntly popular concpt of businss intllignc ar bcoming incrasingly larg. Convntional databas managmnt softwar is no longr fficint nough howvr; paralll databas managmnt systms and massiv data-scal procssing systms lik MapRduc indd look promising. Although, MapRduc is a good option, it is difficult to work with, as th programmr would hav to think at th mappr and rducr lvl. In this papr, w prsnt a simpl yt fficint way to min usful information whr a program can b writtn as a sris of stps. W hav qurid a suprcomputr log fil using Apach s Hadoop and PIG, obtaind rsults as to whn and why th suprcomputr had faild and compard ths rsults to that of a traditional program. Kywords- Big Data, Paralll Procssing, Hadoop, MapRduc, Data Mining, Businss Intllignc, PIG, Log fil analysis, Suprcomputr I. INTRODUCTION Nowadays, businss intllignc has bcom a popular trnd. What usd to b trial and rror has now dvlopd into dcisions basd on past xprincs and othr data. Th fficacy of mining usful information is vr changing and hnc, mor and mor mthods to mak mining fficint ar bing proposd. In this papr, w will propos a mthod to analyz th log fil of a suprcomputr from th bio-informatics background. Log fils of this suprcomputr ar not only vast, but ar also smi-structurd. Th mthod proposd dos not only prov to b mor fficint than th ons alrady xistnt, but also happns to b much simplr. On of th most fficint solutions to vast log fil analysis is using a paralll procssing systm. Howvr, without using a qurying languag, th programmr will nd to hav indpth knowldg about MapRduc. In many situations, th programmr ithr dos not hav such xprinc or would lik a solution in which much training is not rquird. Also, rsultant data is usually rquird within a short intrval as most ral-tim data workd with has high vlocity, which rquirs programmrs to provid instant analysis. W prsnt a solution that allows popl to b abl to min information fficintly on a paralll procssing systm without rquiring much training or background knowldg. As a PIG program is nothing but a sris of stps, or in othr words stpwis instructions, it asy to us, kping in mind th gnral population. W hav implmntd this qurying languag into our solution for analyzing th failurs of a suprcomputr. Corrsponding Author: Madhuri Srinivas Pall Th objctiv of th papr is to analyz th failurs of a suprcomputr by mining its log fil in two ways and to compar th rsults of both th mthods. Th first way is a traditional program writtn in Java that will rad th fil and output th rquird, rlvant information. Th scond mthod is to stor th fils in th HDFS (Hadoop Distributd Fil Systm), writ a PIG qury on MapRduc that will giv th smi-structurd log som schma to work with and output th rquird rsults back to th HDFS. Th xact implmntation has bn xplaind in th rspctiv sction of th papr. By mining both ways, th tim takn by both th mthods for diffrnt sizs of data was compard to s which mthod is mor fficint and fasibl. As hypothsizd, th mthod involving th usag of MapRduc and th PIG qurying languag has provn to b much fastr. Statistics hav bn prsntd in th rsults sction of th papr. In th nxt sction, w hav writtn th background rlvant to our rsarch. This contains a brif dscription about MapRduc and PIG. Sction III and IV contain th nd of th problm statmnt and th implmntation rspctfully. Rsults hav bn portrayd in th Sction V and sction VI contains th conclusion and scop for furthr rsarch. Th papr nds with a list of rfrncs usd for th rsarch of our papr. 2014, IJCSE All Rights Rsrvd 1 II. BACKGROUND Big Data, as pr th Gartnr dfinition, rfrs to data that has on or mor than on of th following thr assts; volum, vlocity and varity. Th analysis of such data is known to b usful in businss intllignc and dcision-

2 making. Ovr th yars, data mining has bn a hot trnd all around th world. Data analysts min larg amounts of data to spot som kind or roccurring trnds that may just provid a comptitiv dg. As mntiond abov, data can b of diffrnt formats. In ordr to analyz smi-structurd and unstructurd data fficintly, w can us Hadoop. Hadoop is an opn sourc softwar framwork. Its architctur can b broadly classifid into two parts; th Hadoop Distributd Fil systm (HDFS), which is it s storag mchanism and th Hadoop distributd computational mchanism, which is popularly known as MapRduc. On a fully configurd Hadoop clustr, thr ar fiv running damons; th namnod, th datanod, th jobtrackr, th tasktrackr and th scondary namnod. Th namnod and datanod hav a mastr-slav rlationship. Th namnod is th mastr, contains fil s mtadata and kps track of which blocks of data go to which nod. Th datanods do th grunt work of rading and writing HDFS data blocks to fil systm. Th scondary namnod taks scrnshots of th namnod at rgular intrvals for failur rcovry purposs. Th jobtrackr and tasktrackr hav rlationship similar to that of th namnod and datanod. Whil th jobtrackr monitors all th tasks, th tasktrackr is rsponsibl for individual tasks. Th ovrall topology is dpictd in th following diagram. Th MapRduc modl s data flow contains two main stps; map and rduc. In brif, th Map stp consists of th mastr nod diving th data input into sub-problms and distributing ths problms to th working nods. Th working nods can furthr sub-divid thir own problm and distributu it amongst thir own working nods in th form of a multi-lvl tr. At any itrativ lvl, th working nod computs th rquird output and rports back to it's mastr. Th Rduc stp is somwhat lik a summary stp. It involvs th collction of all th subproblm outputs, combins thm and producs th originally rquird answr. Togthr, thy form th framwork of a distributd systm. PIG is an xtnsion of Hadoop which is usd to simplify th unncssary complxity of MapRduc. It contains two main componnts; a high-lvl languag PIG Latin and a compilr which is usually Hadoop. Complx tasks can b xplicitly writtn as data flow squncs and hnc maks it asir to writ and maintain any particular program. Th usr is givn th opportunity to concntrat on th smantics of th program rathr than optimization as this is automatically don whil using PIG. Pig quris can b writtn in thr ways. Th first way is by using an intractiv grunt shll. Th scond way is to writ a script fil, which is usually for larg, rptitiv programs and th third way is mbdding th quris in a java program. Also, it runs on two diffrnt mods, th local and th Hadoop mod. In this papr, w ar using PIG in th maprduc mod. Ways of Running Pig Latin Script Mthod Grunt intractiv shll Script fil Embddd quris in java program Gnrally usd for ad hoc data Manually ntr lin by lin Usd for larg, rptitiv pig programs Format: pig myscript.pig Pig srvr class allows any Java program to xcut qury A sampl qury in PIG is givn blow(fig.2.1). Th LOAD command will load th fil path as pr th dfind schma using th dlimitr spac. Hr th data typ takn is th dfault chararray. Th alias grp contains th filtrd tupls according to th condition spcifid. In this qury, pattrn matching was usd to chck th dat of th tupl. Th LIMIT oprator limits th total numbr of tupls to 10 for convninc sak and th alias cntd rprsnts ths 10 tupls. To s what is happning bhind th scns, PIG provids thr diagnostic oprators ILLUSTRATE, EXPLAIN and DESCRIBE. Fig.2.1 Dscription grunt> log= LOAD /usr/mady/projct.log USING PigStorag( ) AS (month,day,tim,info1,info2,info3); grunt> grp= FILTER log BY (day matchs.*30.* ); grunt> cntd= LIMIT grp 10; grunt> ILLUSTRATE cntd; 2014, IJCSE All Rights Rsrvd 2

3 Each sparat command can b distinguishd by th grunt> shll indication. Th scrnshot blow(fig.2.2) shows how a fw of th columns of th schma chang by using th ILLUSTRATE command. and a mssag. W hav takn ach pic of information as a column by dfining a schma. Fig.4.1 Fig.2.2 On of th advantags of PIG ovr SQL is that it has a loosr schma approach than that of SQL, which thrfor, maks it mor suitabl for smi-structurd and unstructurd data. III. IMPORTANCE OF THE STUDY PROBLEM STATEMENT Analysis of big data has bcom of utmost priority today. Spcifically, analysis of log fils is rquird in ordr to gaug and undrstand th failurs of th suprcomputr. By doing so, futur failurs and losss can b prvntd. In this papr, w hav proposd an fficint solution for analyzing th log fils of a suprcomputr compard to that of a traditional program. W hav also takn into considration th fact that popl attmpting to analyz ths fils may not hav much xprinc and knowldg in th MapRduc domain. Th solution proposd in this papr is important as it can b usd b a larg population, not just tchnical xprts. W hav targtd a mor divrs population as potntial usrs of this proposd solution. Also, this is an apt solution for not just structurd logs, but smi-structurd logs as wll. Using pig w can comput on distributd nvironmnt so how much vr is th siz of th input w can comput th rsults. IV. IMPLEMENTATION W hav usd PIG on Hadoop vrsion Firstly, w hav takn a sampl smi-structurd suprcomputr log fil, a part of which is shown blow(fig.4.1). In this fil, thr is information lik th dat, tim, krnl in qustion Th log fil was copid to th HDFS. Whil writing th qury, w sarchd for all logs in which th suprcomputr had faild. Th qury was xcutd in th MapRduc mod of PIG and was tstd on th intractiv grunt shll as wll as by saving it as a script fil. Aftr MapRduc functions wr prformd, ths logs wr stord in a output fil in th HDFS for viwing. W had also prformd quris for usr-dfind quris such as which activitis wr going on at a particular usr-dfind tim or on a usr-dfind dat. A scrnshot of th statistics for a sampl fil ar shown blow(fig.4.2). Fig.4.2 As sn in th statistics scrnshot, various masurs hav bn displayd including th job_id, maps, rducs, maximum map tim, minimum map tim, avrag map tim, mdian map tim, maximum rduc tim, minimum rduc tim, avrag rduc tim, mdian rduc tim, aliass and fatur outputs. Also, various countrs hav bn displayd such as th total numbr of rcords writtn. 2014, IJCSE All Rights Rsrvd 3

4 To viw th output rcords of th qury, w accss th HDFS. Blow(fig.4.3) is th output of th sampl log fil. Blow (tabl 5.2) ar th valus takn to draw th graph. In th Hadoop mthod, a fw initial valus ar unstabl. This is probably du to th diffrnc in numbr of maps allottd, th Hadoop nvironmnt and numbr of rsourcs availabl. Th traditional mthod shows no such inconsistncy. Fig.4.3 In this papr, w hav dtrmind th fficincy of using PIG compard to that of a traditional Java program by taking diffrnt sizd log fils and analyzing thm both ways. Each tim, w doubld th siz of th log fil in ordr to gt an xact pictur as to which mthod is suitabl for which kind of fil (i.. small, mdium, larg). Th rsults of th prformanc chck ar givn in th nxt sction of th papr. V. RESULT & ANALYSIS On taking diffrnt sizd fils, w hav obsrvd that by using Hadoop and PIG, log fils can b analyzd much mor fficintly compard to that of a traditional Java program. Not only dos th tim takn for th fil to b analyzd rduc considrably by using this mthod, but th complxity of programming with this mthod has also provn to b much mor usr-frindly. As Hadoop s architctur uss paralll procssing, for largr fils it is much mor fasibl. That is, to procss largr fils, mor maps can b usd in ordr to maintain fficincy. Th graph shown in th nxt column (fig.5.1) was mad by rcording th tim takn by diffrnt sizd fils to hav thir logs procssd in millisconds. This was don for both th mthods in our comparison. s ) d n c o ilis ( m tim n tio c u x E analyzing compltly unstructurd data. P r fo r m an c C o m p a ri si o n Fig.5.1 Siz (mgabyt) Traditional mthod (milli sc) Hadoop (milli sc) VI. Tabl 5.2 CONCLUSION According to xprimntal rsults, th mthod involving using th qurying languag PIG on Hadoop has provn to b scalabl, rliabl,fastr,and fficint. On of th major advantags bsids fficincy is th fact that it is simpl and asy to us, hnc targting a widr audinc. Th mthod rliably analyzs th log fils, and hnc provs that it is usful for structurd as wll as smi-structurd data. Extnsions of this papr could includ a solution for REFERENCES [1]. T. Whit, Hadoop: Th Dfinitiv Guid. Yahoo Prss,2010. [2]. Chuck Lam,Pig:Hadoop in Action. [3]. J. Dan and S. Ghmawat, Maprduc: Simplifid Data Procssing on Larg Clustrs, Comm. of th ACM,Vol. 51, no. 1, pp , [4]. C. Olston, B. Rd, U. Srivastava, R. Kumar, and A. Tomkins, Pig latin: A Not-So-Forign Languag for Data Procssing, Proc. of th 2008 ACM SIGMOD intrnational confrncon Managmnt of Data, 2008, pp [5]. Thomas Ridmistr, Mohammad Ahmad Munawar, Miao Jiang, Paul A.S.Ward, "Diagnosis of Rcurrnt 2014, IJCSE All Rights Rsrvd 4

5 Faults using Log Fils," Proc. of th 2009 Confrnc of th Cntr for Advancd Studis on Collaborativ Rsarch,Novmbr 2009, pp [6]. Apach. Hadoop: Opn-sourc implmntation of MapRduc. [7]. Apach. Pig: High-lvl data ow systm for Hadoop. [8]. Michal Cardosa, Chnyu Wang, Anshuman Nangia, Abhishk Chandra, Jon Wissman,"Exploring MapRduc fficincy with highly-distributd data" Proc. of th scond intrnational workshop on MapRduc and its applications",jun 2011, pp [9]. H.-C. Yang, A. Dasdan, R.-L. Hsiao, and D. S. Parkr, Map-rducmrg: simplifid rlational data procssing on larg clustrs, proc. of th SIGMOD Confrnc, 2007, pp [10]. A. Gats, O. Natkovich, S. Chopra, P. Kamath, S. Narayanam, C. Olston, B. Rd, S. Srinivasan, and U. Srivastava., Building a High-Lvl Dataflow Systm on Top of Map-Rduc: Th Pig Exprinc. Proc. of th VLDB Endowmnt, vol. 2,no. 2, [11]. A tutorial on pig: , IJCSE All Rights Rsrvd 5

The example is taken from Sect. 1.2 of Vol. 1 of the CPN book.

The example is taken from Sect. 1.2 of Vol. 1 of the CPN book. Rsourc Allocation Abstract This is a small toy xampl which is wll-suitd as a first introduction to Cnts. Th CN modl is dscribd in grat dtail, xplaining th basic concpts of C-nts. Hnc, it can b rad by popl

More information

C H A P T E R 1 Writing Reports with SAS

C H A P T E R 1 Writing Reports with SAS C H A P T E R 1 Writing Rports with SAS Prsnting information in a way that s undrstood by th audinc is fundamntally important to anyon s job. Onc you collct your data and undrstand its structur, you nd

More information

EFFECT OF GEOMETRICAL PARAMETERS ON HEAT TRANSFER PERFORMACE OF RECTANGULAR CIRCUMFERENTIAL FINS

EFFECT OF GEOMETRICAL PARAMETERS ON HEAT TRANSFER PERFORMACE OF RECTANGULAR CIRCUMFERENTIAL FINS 25 Vol. 3 () January-March, pp.37-5/tripathi EFFECT OF GEOMETRICAL PARAMETERS ON HEAT TRANSFER PERFORMACE OF RECTANGULAR CIRCUMFERENTIAL FINS *Shilpa Tripathi Dpartmnt of Chmical Enginring, Indor Institut

More information

5 2 index. e e. Prime numbers. Prime factors and factor trees. Powers. worked example 10. base. power

5 2 index. e e. Prime numbers. Prime factors and factor trees. Powers. worked example 10. base. power Prim numbrs W giv spcial nams to numbrs dpnding on how many factors thy hav. A prim numbr has xactly two factors: itslf and 1. A composit numbr has mor than two factors. 1 is a spcial numbr nithr prim

More information

Architecture of the proposed standard

Architecture of the proposed standard Architctur of th proposd standard Introduction Th goal of th nw standardisation projct is th dvlopmnt of a standard dscribing building srvics (.g.hvac) product catalogus basd on th xprincs mad with th

More information

An Broad outline of Redundant Array of Inexpensive Disks Shaifali Shrivastava 1 Department of Computer Science and Engineering AITR, Indore

An Broad outline of Redundant Array of Inexpensive Disks Shaifali Shrivastava 1 Department of Computer Science and Engineering AITR, Indore Intrnational Journal of mrging Tchnology and dvancd nginring Wbsit: www.ijta.com (ISSN 2250-2459, Volum 2, Issu 4, pril 2012) n road outlin of Rdundant rray of Inxpnsiv isks Shaifali Shrivastava 1 partmnt

More information

FACULTY SALARIES FALL 2004. NKU CUPA Data Compared To Published National Data

FACULTY SALARIES FALL 2004. NKU CUPA Data Compared To Published National Data FACULTY SALARIES FALL 2004 NKU CUPA Data Compard To Publishd National Data May 2005 Fall 2004 NKU Faculty Salaris Compard To Fall 2004 Publishd CUPA Data In th fall 2004 Northrn Kntucky Univrsity was among

More information

A Project Management framework for Software Implementation Planning and Management

A Project Management framework for Software Implementation Planning and Management PPM02 A Projct Managmnt framwork for Softwar Implmntation Planning and Managmnt Kith Lancastr Lancastr Stratgis Kith.Lancastr@LancastrStratgis.com Th goal of introducing nw tchnologis into your company

More information

by John Donald, Lecturer, School of Accounting, Economics and Finance, Deakin University, Australia

by John Donald, Lecturer, School of Accounting, Economics and Finance, Deakin University, Australia Studnt Nots Cost Volum Profit Analysis by John Donald, Lcturr, School of Accounting, Economics and Financ, Dakin Univrsity, Australia As mntiond in th last st of Studnt Nots, th ability to catgoris costs

More information

Cisco Data Virtualization

Cisco Data Virtualization Cisco Data Virtualization Big Data Eco-systm Discussion with Bloor Group Bob Ev, David Bsmr July 2014 Cisco Data Virtualization Backgroundr Cisco Data Virtualization is agil data intgration softwar that

More information

Adverse Selection and Moral Hazard in a Model With 2 States of the World

Adverse Selection and Moral Hazard in a Model With 2 States of the World Advrs Slction and Moral Hazard in a Modl With 2 Stats of th World A modl of a risky situation with two discrt stats of th world has th advantag that it can b natly rprsntd using indiffrnc curv diagrams,

More information

QUANTITATIVE METHODS CLASSES WEEK SEVEN

QUANTITATIVE METHODS CLASSES WEEK SEVEN QUANTITATIVE METHODS CLASSES WEEK SEVEN Th rgrssion modls studid in prvious classs assum that th rspons variabl is quantitativ. Oftn, howvr, w wish to study social procsss that lad to two diffrnt outcoms.

More information

Category 7: Employee Commuting

Category 7: Employee Commuting 7 Catgory 7: Employ Commuting Catgory dscription This catgory includs missions from th transportation of mploys 4 btwn thir homs and thir worksits. Emissions from mploy commuting may aris from: Automobil

More information

Information Management Strategy: Exploiting Big data and Advanced Analytics

Information Management Strategy: Exploiting Big data and Advanced Analytics Information Managmnt Stratgy: Exploiting Big data and Advancd Analytics William Duply Stratgist HP Cloud Hwltt-Packard Canada Big : What w ar building: Actionabl intllignc Businss valu Traditional BI/MIS/CRM/

More information

Data warehouse on Manpower Employment for Decision Support System

Data warehouse on Manpower Employment for Decision Support System Data warhous on Manpowr Employmnt for Dcision Support Systm Amro F. ALASTA, and Muftah A. Enaba Abstract Sinc th us of computrs in businss world, data collction has bcom on of th most important issus du

More information

Keywords Cloud Computing, Service level agreement, cloud provider, business level policies, performance objectives.

Keywords Cloud Computing, Service level agreement, cloud provider, business level policies, performance objectives. Volum 3, Issu 6, Jun 2013 ISSN: 2277 128X Intrnational Journal of Advancd Rsarch in Computr Scinc and Softwar Enginring Rsarch Papr Availabl onlin at: wwwijarcsscom Dynamic Ranking and Slction of Cloud

More information

WORKERS' COMPENSATION ANALYST, 1774 SENIOR WORKERS' COMPENSATION ANALYST, 1769

WORKERS' COMPENSATION ANALYST, 1774 SENIOR WORKERS' COMPENSATION ANALYST, 1769 08-16-85 WORKERS' COMPENSATION ANALYST, 1774 SENIOR WORKERS' COMPENSATION ANALYST, 1769 Summary of Dutis : Dtrmins City accptanc of workrs' compnsation cass for injurd mploys; authorizs appropriat tratmnt

More information

Key Management System Framework for Cloud Storage Singa Suparman, Eng Pin Kwang Temasek Polytechnic {singas,engpk}@tp.edu.sg

Key Management System Framework for Cloud Storage Singa Suparman, Eng Pin Kwang Temasek Polytechnic {singas,engpk}@tp.edu.sg Ky Managmnt Systm Framwork for Cloud Storag Singa Suparman, Eng Pin Kwang Tmask Polytchnic {singas,ngpk}@tp.du.sg Abstract In cloud storag, data ar oftn movd from on cloud storag srvic to anothr. Mor frquntly

More information

Sci.Int.(Lahore),26(1),131-138,2014 ISSN 1013-5316; CODEN: SINTE 8 131

Sci.Int.(Lahore),26(1),131-138,2014 ISSN 1013-5316; CODEN: SINTE 8 131 Sci.Int.(Lahor),26(1),131-138,214 ISSN 113-5316; CODEN: SINTE 8 131 REQUIREMENT CHANGE MANAGEMENT IN AGILE OFFSHORE DEVELOPMENT (RCMAOD) 1 Suhail Kazi, 2 Muhammad Salman Bashir, 3 Muhammad Munwar Iqbal,

More information

Scalable Transactions for Web Applications in the Cloud using Customized CloudTPS

Scalable Transactions for Web Applications in the Cloud using Customized CloudTPS Shashikant Mahadu Bankar/ (IJCSIT) Intrnational Journal of Computr Scinc and Information Tchnologis, Vol. (3), 2015, 218-2191 Scalabl Transactions for Wb Applications in th Cloud using Customizd CloudTPS

More information

SOFTWARE ENGINEERING AND APPLIED CRYPTOGRAPHY IN CLOUD COMPUTING AND BIG DATA

SOFTWARE ENGINEERING AND APPLIED CRYPTOGRAPHY IN CLOUD COMPUTING AND BIG DATA Intrnational Journal on Tchnical and Physical Problms of Enginring (IJTPE) Publishd by Intrnational Organization of IOTPE ISSN 077-358 IJTPE Journal www.iotp.com ijtp@iotp.com Sptmbr 015 Issu 4 Volum 7

More information

Lecture 20: Emitter Follower and Differential Amplifiers

Lecture 20: Emitter Follower and Differential Amplifiers Whits, EE 3 Lctur 0 Pag of 8 Lctur 0: Emittr Followr and Diffrntial Amplifirs Th nxt two amplifir circuits w will discuss ar ry important to lctrical nginring in gnral, and to th NorCal 40A spcifically.

More information

Continuity Cloud Virtual Firewall Guide

Continuity Cloud Virtual Firewall Guide Cloud Virtual Firwall Guid uh6 Vrsion 1.0 Octobr 2015 Foldr BDR Guid for Vam Pag 1 of 36 Cloud Virtual Firwall Guid CONTENTS INTRODUCTION... 3 ACCESSING THE VIRTUAL FIREWALL... 4 HYPER-V/VIRTUALBOX CONTINUITY

More information

Product Overview. Version 1-12/14

Product Overview. Version 1-12/14 Product Ovrviw Vrsion 1-12/14 W ar Grosvnor Tchnology Accss Control Solutions W dvlop, manufactur and provid accss control and workforc managmnt solutions th world ovr. Our product offring ompasss hardwar,

More information

Question 3: How do you find the relative extrema of a function?

Question 3: How do you find the relative extrema of a function? ustion 3: How do you find th rlativ trma of a function? Th stratgy for tracking th sign of th drivativ is usful for mor than dtrmining whr a function is incrasing or dcrasing. It is also usful for locating

More information

The international Internet site of the geoviticulture MCC system Le site Internet international du système CCM géoviticole

The international Internet site of the geoviticulture MCC system Le site Internet international du système CCM géoviticole Th intrnational Intrnt sit of th goviticultur MCC systm L sit Intrnt intrnational du systèm CCM géoviticol Flávio BELLO FIALHO 1 and Jorg TONIETTO 1 1 Rsarchr, Embrapa Uva Vinho, Caixa Postal 130, 95700-000

More information

CARE QUALITY COMMISSION ESSENTIAL STANDARDS OF QUALITY AND SAFETY. Outcome 10 Regulation 11 Safety and Suitability of Premises

CARE QUALITY COMMISSION ESSENTIAL STANDARDS OF QUALITY AND SAFETY. Outcome 10 Regulation 11 Safety and Suitability of Premises CARE QUALITY COMMISSION ESSENTIAL STANDARDS OF QUALITY AND SAFETY Outcom 10 Rgulation 11 Safty and Suitability of Prmiss CQC Rf 10A 10A(1) Lad Dirctor / Lad Officr Rspons Impact Liklihood Lvl of Concrn

More information

Keynote Speech Collaborative Web Services and Peer-to-Peer Grids

Keynote Speech Collaborative Web Services and Peer-to-Peer Grids Kynot Spch Collaborativ s and Pr-to-Pr Grids Goffry ox 1,2,4, Hasan Bulut 2, Kangsok Kim 2, Sung-Hoon Ko 1, Sangmi L 5, Sangyoon h 2, Shridp Pallickara 1, Xiaohong Qiu 1,3, Ahmt yar 1,3, Minjun Wang 1,3,

More information

June 2012. Enprise Rent. Enprise 1.1.6. Author: Document Version: Product: Product Version: SAP Version: 8.81.100 8.8

June 2012. Enprise Rent. Enprise 1.1.6. Author: Document Version: Product: Product Version: SAP Version: 8.81.100 8.8 Jun 22 Enpris Rnt Author: Documnt Vrsion: Product: Product Vrsion: SAP Vrsion: Enpris Enpris Rnt 88 88 Enpris Rnt 22 Enpris Solutions All rights rsrvd No parts of this work may b rproducd in any form or

More information

Enforcing Fine-grained Authorization Policies for Java Mobile Agents

Enforcing Fine-grained Authorization Policies for Java Mobile Agents Enforcing Fin-graind Authorization Policis for Java Mobil Agnts Giovanni Russllo Changyu Dong Narankr Dulay Dpartmnt of Computing Imprial Collg London South Knsington London, SW7 2AZ, UK {g.russllo, changyu.dong,

More information

Lecture 3: Diffusion: Fick s first law

Lecture 3: Diffusion: Fick s first law Lctur 3: Diffusion: Fick s first law Today s topics What is diffusion? What drivs diffusion to occur? Undrstand why diffusion can surprisingly occur against th concntration gradint? Larn how to dduc th

More information

Development of Financial Management Reporting in MPLS

Development of Financial Management Reporting in MPLS 1 Dvlopmnt of Financial Managmnt Rporting in MPLS 1. Aim Our currnt financial rports ar structurd to dlivr an ovrall financial pictur of th dpartmnt in it s ntirty, and thr is no attmpt to provid ithr

More information

Review and Analysis of Cloud Computing Quality of Experience

Review and Analysis of Cloud Computing Quality of Experience Rviw and Analysis of Cloud Computing Quality of Exprinc Fash Safdari and Victor Chang School of Computing, Crativ Tchnologis and Enginring, Lds Mtropolitan Univrsity, Hadinly, Lds LS6 3QR, U.K. {F.Safdari,

More information

Planning and Managing Copper Cable Maintenance through Cost- Benefit Modeling

Planning and Managing Copper Cable Maintenance through Cost- Benefit Modeling Planning and Managing Coppr Cabl Maintnanc through Cost- Bnfit Modling Jason W. Rup U S WEST Advancd Tchnologis Bouldr Ky Words: Maintnanc, Managmnt Stratgy, Rhabilitation, Cost-bnfit Analysis, Rliability

More information

AP Calculus AB 2008 Scoring Guidelines

AP Calculus AB 2008 Scoring Guidelines AP Calculus AB 8 Scoring Guidlins Th Collg Board: Conncting Studnts to Collg Succss Th Collg Board is a not-for-profit mmbrship association whos mission is to connct studnts to collg succss and opportunity.

More information

ITIL & Service Predictability/Modeling. 2006 Plexent

ITIL & Service Predictability/Modeling. 2006 Plexent ITIL & Srvic Prdictability/Modling 1 2 Plxnt Th Company 2001 Foundd Plxnt basd on an Expandd ITIL Architctur, CMMI, ISO, and BS15000 - itdna 2003 Launchd itdna Srvic Offring 2003 John Groom, past Dirctor

More information

Constraint-Based Analysis of Gene Deletion in a Metabolic Network

Constraint-Based Analysis of Gene Deletion in a Metabolic Network Constraint-Basd Analysis of Gn Dltion in a Mtabolic Ntwork Abdlhalim Larhlimi and Alxandr Bockmayr DFG-Rsarch Cntr Mathon, FB Mathmatik und Informatik, Fri Univrsität Brlin, Arnimall, 3, 14195 Brlin, Grmany

More information

IHE IT Infrastructure (ITI) Technical Framework Supplement. Cross-Enterprise Document Workflow (XDW) Trial Implementation

IHE IT Infrastructure (ITI) Technical Framework Supplement. Cross-Enterprise Document Workflow (XDW) Trial Implementation Intgrating th Halthcar Entrpris 5 IHE IT Infrastructur (ITI) Tchnical Framwork Supplmnt 10 Cross-Entrpris Documnt Workflow (XDW) 15 Trial Implmntation 20 Dat: Octobr 13, 2014 Author: IHE ITI Tchnical Committ

More information

IBM Healthcare Home Care Monitoring

IBM Healthcare Home Care Monitoring IBM Halthcar Hom Car Monitoring Sptmbr 30th, 2015 by Sal P. Causi, P. Eng. IBM Halthcar Businss Dvlopmnt Excutiv scausi@ca.ibm.com IBM Canada Cloud Computing Tigr Tam Homcar by dfinition 1. With a gnsis

More information

A Loadable Task Execution Recorder for Hierarchical Scheduling in Linux

A Loadable Task Execution Recorder for Hierarchical Scheduling in Linux A Loadabl Task Excution Rcordr for Hirarchical Schduling in Linux Mikal Åsbrg and Thomas Nolt MRTC/Mälardaln Univrsity PO Box 883, SE-721 23, Västrås, Swdn {mikalasbrg,thomasnolt@mdhs Shinpi Kato Carngi

More information

Econ 371: Answer Key for Problem Set 1 (Chapter 12-13)

Econ 371: Answer Key for Problem Set 1 (Chapter 12-13) con 37: Answr Ky for Problm St (Chaptr 2-3) Instructor: Kanda Naknoi Sptmbr 4, 2005. (2 points) Is it possibl for a country to hav a currnt account dficit at th sam tim and has a surplus in its balanc

More information

Hardware Modules of the RSA Algorithm

Hardware Modules of the RSA Algorithm SERBIAN JOURNAL OF ELECTRICAL ENGINEERING Vol. 11, No. 1, Fbruary 2014, 121-131 UDC: 004.3`142:621.394.14 DOI: 10.2298/SJEE140114011S Hardwar Moduls of th RSA Algorithm Vlibor Škobić 1, Branko Dokić 1,

More information

Gold versus stock investment: An econometric analysis

Gold versus stock investment: An econometric analysis Intrnational Journal of Dvlopmnt and Sustainability Onlin ISSN: 268-8662 www.isdsnt.com/ijds Volum Numbr, Jun 202, Pag -7 ISDS Articl ID: IJDS20300 Gold vrsus stock invstmnt: An conomtric analysis Martin

More information

Parallel and Distributed Programming. Performance Metrics

Parallel and Distributed Programming. Performance Metrics Paralll and Distributd Programming Prformanc! wo main goals to b achivd with th dsign of aralll alications ar:! Prformanc: th caacity to rduc th tim to solv th roblm whn th comuting rsourcs incras;! Scalability:

More information

Remember you can apply online. It s quick and easy. Go to www.gov.uk/advancedlearningloans. Title. Forename(s) Surname. Sex. Male Date of birth D

Remember you can apply online. It s quick and easy. Go to www.gov.uk/advancedlearningloans. Title. Forename(s) Surname. Sex. Male Date of birth D 24+ Advancd Larning Loan Application form Rmmbr you can apply onlin. It s quick and asy. Go to www.gov.uk/advancdlarningloans About this form Complt this form if: you r studying an ligibl cours at an approvd

More information

Combinatorial Analysis of Network Security

Combinatorial Analysis of Network Security Combinatorial Analysis of Ntwork Scurity Stvn Nol a, Brian O Brry a, Charls Hutchinson a, Sushil Jajodia a, Lynn Kuthan b, and Andy Nguyn b a Gorg Mason Univrsity Cntr for Scur Information Systms b Dfns

More information

Entity-Relationship Model

Entity-Relationship Model Entity-Rlationship Modl Kuang-hua Chn Dpartmnt of Library and Information Scinc National Taiwan Univrsity A Company Databas Kps track of a company s mploys, dpartmnts and projcts Aftr th rquirmnts collction

More information

STATEMENT OF INSOLVENCY PRACTICE 3.2

STATEMENT OF INSOLVENCY PRACTICE 3.2 STATEMENT OF INSOLVENCY PRACTICE 3.2 COMPANY VOLUNTARY ARRANGEMENTS INTRODUCTION 1 A Company Voluntary Arrangmnt (CVA) is a statutory contract twn a company and its crditors undr which an insolvncy practitionr

More information

Category 1: Purchased Goods and Services

Category 1: Purchased Goods and Services 1 Catgory 1: Purchasd Goods and Srvics Catgory dscription T his catgory includs all upstram (i.., cradl-to-gat) missions from th production of products purchasd or acquird by th rporting company in th

More information

Fleet vehicles opportunities for carbon management

Fleet vehicles opportunities for carbon management Flt vhicls opportunitis for carbon managmnt Authors: Kith Robrtson 1 Dr. Kristian Stl 2 Dr. Christoph Hamlmann 3 Alksandra Krukar 4 Tdla Mzmir 5 1 Snior Sustainability Consultant & Lad Analyst, Arup 2

More information

Nimble Storage Exchange 2010 40,000-Mailbox Resiliency Storage Solution

Nimble Storage Exchange 2010 40,000-Mailbox Resiliency Storage Solution Nimbl Storag Exchang 2010 40,0-Mailbox Rsilincy Storag Solution Tstd with: ESRP Storag Vrsion 3.0 Tst dat: July 10, 2012 Ovrviw This documnt provids information on Nimbl Storag's storag solution for Microsoft

More information

A Secure Web Services for Location Based Services in Wireless Networks*

A Secure Web Services for Location Based Services in Wireless Networks* A Scur Wb Srvics for Location Basd Srvics in Wirlss Ntworks* Minsoo L 1, Jintak Kim 1, Shyun Park 1, Jail L 2 and Sokla L 21 1 School of Elctrical and Elctronics Enginring, Chung-Ang Univrsity, 221, HukSuk-Dong,

More information

A Multi-Heuristic GA for Schedule Repair in Precast Plant Production

A Multi-Heuristic GA for Schedule Repair in Precast Plant Production From: ICAPS-03 Procdings. Copyright 2003, AAAI (www.aaai.org). All rights rsrvd. A Multi-Huristic GA for Schdul Rpair in Prcast Plant Production Wng-Tat Chan* and Tan Hng W** *Associat Profssor, Dpartmnt

More information

SPECIAL VOWEL SOUNDS

SPECIAL VOWEL SOUNDS SPECIAL VOWEL SOUNDS Plas consult th appropriat supplmnt for th corrsponding computr softwar lsson. Rfr to th 42 Sounds Postr for ach of th Spcial Vowl Sounds. TEACHER INFORMATION: Spcial Vowl Sounds (SVS)

More information

REPORT' Meeting Date: April 19,201 2 Audit Committee

REPORT' Meeting Date: April 19,201 2 Audit Committee REPORT' Mting Dat: April 19,201 2 Audit Committ For Information DATE: March 21,2012 REPORT TITLE: FROM: Paul Wallis, CMA, CIA, CISA, Dirctor, Intrnal Audit OBJECTIVE To inform Audit Committ of th rsults

More information

ESA Support to ESTB Users

ESA Support to ESTB Users ESA Support to ESTB Usrs Dr. Javir Vntura-Travst Europan Spac Agncy 3 rd ESTB Workshop Nic 12 Novmbr 2002 OUTLINE! ESA ESTB wbsit support! Th ESTB/EGNOS Hlpdsk! Th ESTB Nwslttr! Th ESA SISNET tchnology!

More information

Real-Time Evaluation of Email Campaign Performance

Real-Time Evaluation of Email Campaign Performance Singapor Managmnt Univrsity Institutional Knowldg at Singapor Managmnt Univrsity Rsarch Collction L Kong Chian School Of Businss L Kong Chian School of Businss 10-2008 Ral-Tim Evaluation of Email Campaign

More information

Rural and Remote Broadband Access: Issues and Solutions in Australia

Rural and Remote Broadband Access: Issues and Solutions in Australia Rural and Rmot Broadband Accss: Issus and Solutions in Australia Dr Tony Warrn Group Managr Rgulatory Stratgy Tlstra Corp Pag 1 Tlstra in confidnc Ovrviw Australia s gographical siz and population dnsity

More information

Use a high-level conceptual data model (ER Model). Identify objects of interest (entities) and relationships between these objects

Use a high-level conceptual data model (ER Model). Identify objects of interest (entities) and relationships between these objects Chaptr 3: Entity Rlationship Modl Databas Dsign Procss Us a high-lvl concptual data modl (ER Modl). Idntify objcts of intrst (ntitis) and rlationships btwn ths objcts Idntify constraints (conditions) End

More information

Mathematics. Mathematics 3. hsn.uk.net. Higher HSN23000

Mathematics. Mathematics 3. hsn.uk.net. Higher HSN23000 hsn uknt Highr Mathmatics UNIT Mathmatics HSN000 This documnt was producd spcially for th HSNuknt wbsit, and w rquir that any copis or drivativ works attribut th work to Highr Still Nots For mor dtails

More information

EVALUATING EFFICIENCY OF SERVICE SUPPLY CHAIN USING DEA (CASE STUDY: AIR AGENCY)

EVALUATING EFFICIENCY OF SERVICE SUPPLY CHAIN USING DEA (CASE STUDY: AIR AGENCY) Indian Journal Fundamntal and Applid Lif Scincs ISSN: 22 64 (Onlin) An Opn Accss, Onlin Intrnational Journal Availabl at www.cibtch.org/sp.d/jls/20/0/jls.htm 20 Vol. (S), pp. 466-47/Shams and Ghafouripour

More information

CPS 220 Theory of Computation REGULAR LANGUAGES. Regular expressions

CPS 220 Theory of Computation REGULAR LANGUAGES. Regular expressions CPS 22 Thory of Computation REGULAR LANGUAGES Rgular xprssions Lik mathmatical xprssion (5+3) * 4. Rgular xprssion ar built using rgular oprations. (By th way, rgular xprssions show up in various languags:

More information

Sharp bounds for Sándor mean in terms of arithmetic, geometric and harmonic means

Sharp bounds for Sándor mean in terms of arithmetic, geometric and harmonic means Qian t al. Journal of Inqualitis and Applications (015) 015:1 DOI 10.1186/s1660-015-0741-1 R E S E A R C H Opn Accss Sharp bounds for Sándor man in trms of arithmtic, gomtric and harmonic mans Wi-Mao Qian

More information

Probabilistic maintenance and asset management on moveable storm surge barriers

Probabilistic maintenance and asset management on moveable storm surge barriers Probabilistic maintnanc an asst managmnt on movabl storm surg barrirs Patrick Wbbrs Ministry of Transport, Public Works an Watr Managmnt Civil Enginring Division A n a l y s O n r h o u F a a l k a n s

More information

Meerkats: A Power-Aware, Self-Managing Wireless Camera Network for Wide Area Monitoring

Meerkats: A Power-Aware, Self-Managing Wireless Camera Network for Wide Area Monitoring Mrkats: A Powr-Awar, Slf-Managing Wirlss Camra Ntwork for Wid Ara Monitoring C. B. Margi 1, X. Lu 1, G. Zhang 1, G. Stank 2, R. Manduchi 1, K. Obraczka 1 1 Dpartmnt of Computr Enginring, Univrsity of California,

More information

(Analytic Formula for the European Normal Black Scholes Formula)

(Analytic Formula for the European Normal Black Scholes Formula) (Analytic Formula for th Europan Normal Black Schols Formula) by Kazuhiro Iwasawa Dcmbr 2, 2001 In this short summary papr, a brif summary of Black Schols typ formula for Normal modl will b givn. Usually

More information

Why An Event App... Before You Start... Try A Few Apps... Event Management Features... Generate Revenue... Vendors & Questions to Ask...

Why An Event App... Before You Start... Try A Few Apps... Event Management Features... Generate Revenue... Vendors & Questions to Ask... Mo b i l E v ntap pgui d : Ho wt op ur c ha t hb te v ntap p f o ry o ura o c i a t i o n T he nt i a l Gui d t oe v ntap p E v nt nt i a l b y Tabl of Contnt Why An Evnt App......... o Whr to Start With

More information

Upper Bounding the Price of Anarchy in Atomic Splittable Selfish Routing

Upper Bounding the Price of Anarchy in Atomic Splittable Selfish Routing Uppr Bounding th Pric of Anarchy in Atomic Splittabl Slfish Routing Kamyar Khodamoradi 1, Mhrdad Mahdavi, and Mohammad Ghodsi 3 1 Sharif Univrsity of Tchnology, Thran, Iran, khodamoradi@c.sharif.du Sharif

More information

A Theoretical Model of Public Response to the Homeland Security Advisory System

A Theoretical Model of Public Response to the Homeland Security Advisory System A Thortical Modl of Public Rspons to th Homland Scurity Advisory Systm Amy (Wnxuan) Ding Dpartmnt of Information and Dcision Scincs Univrsity of Illinois Chicago, IL 60607 wxding@uicdu Using a diffrntial

More information

union scholars program APPLICATION DEADLINE: FEBRUARY 28 YOU CAN CHANGE THE WORLD... AND EARN MONEY FOR COLLEGE AT THE SAME TIME!

union scholars program APPLICATION DEADLINE: FEBRUARY 28 YOU CAN CHANGE THE WORLD... AND EARN MONEY FOR COLLEGE AT THE SAME TIME! union scholars YOU CAN CHANGE THE WORLD... program AND EARN MONEY FOR COLLEGE AT THE SAME TIME! AFSCME Unitd Ngro Collg Fund Harvard Univrsity Labor and Worklif Program APPLICATION DEADLINE: FEBRUARY 28

More information

User-Perceived Quality of Service in Hybrid Broadcast and Telecommunication Networks

User-Perceived Quality of Service in Hybrid Broadcast and Telecommunication Networks Usr-Prcivd Quality of Srvic in Hybrid Broadcast and Tlcommunication Ntworks Michal Galtzka Fraunhofr Institut for Intgratd Circuits Branch Lab Dsign Automation, Drsdn, Grmany Michal.Galtzka@as.iis.fhg.d

More information

LG has introduced the NeON 2, with newly developed Cello Technology which improves performance and reliability. Up to 320W 300W

LG has introduced the NeON 2, with newly developed Cello Technology which improves performance and reliability. Up to 320W 300W Cllo Tchnology LG has introducd th NON 2, with nwly dvlopd Cllo Tchnology which improvs prformanc and rliability. Up to 320W 300W Cllo Tchnology Cll Connction Elctrically Low Loss Low Strss Optical Absorption

More information

Precise Memory Leak Detection for Java Software Using Container Profiling

Precise Memory Leak Detection for Java Software Using Container Profiling Distinguishd Papr Prcis Mmory Lak Dtction for Java Softwar Using Containr Profiling Guoqing Xu Atanas Rountv Dpartmnt of Computr Scinc and Enginring Ohio Stat Univrsity {xug,rountv}@cs.ohio-stat.du ABSTRACT

More information

ONLINE CONSUMER BEHAVIOR: AN EXPLORATORY STUDY

ONLINE CONSUMER BEHAVIOR: AN EXPLORATORY STUDY G.J. C.M.P., Vol. 2(4):98-17 July-August, 213 ISSN: 2319 7285 ONLINE CONSUMER BEHAVIOR: AN EXPLORATORY STUDY Namita Bhandari* & Prti Kaushal** *Panjab Univrsity, Chandigarh, India **Chitkara Univrsity,

More information

I/O Deduplication: Utilizing Content Similarity to Improve I/O Performance

I/O Deduplication: Utilizing Content Similarity to Improve I/O Performance I/O Dduplication: Utilizing Contnt Similarity to Improv I/O Prformanc Ricardo Kollr Raju Rangaswami rkoll001@cs.fiu.du raju@cs.fiu.du School of Computing and Information Scincs, Florida Intrnational Univrsity

More information

An IAC Approach for Detecting Profile Cloning in Online Social Networks

An IAC Approach for Detecting Profile Cloning in Online Social Networks An IAC Approach for Dtcting Profil Cloning in Onlin Social Ntworks MortzaYousfi Kharaji 1 and FatmhSalhi Rizi 2 1 Dptartmnt of Computr and Information Tchnology Enginring,Mazandaran of Scinc and Tchnology,Babol,

More information

Voice Biometrics: How does it work? Konstantin Simonchik

Voice Biometrics: How does it work? Konstantin Simonchik Voic Biomtrics: How dos it work? Konstantin Simonchik Lappnranta, 4 Octobr 2012 Voicprint Makup Fingrprint Facprint Lik a ingrprint or acprint, a voicprint also has availabl paramtrs that provid uniqu

More information

Usability Test of UCRS e-learning DVD

Usability Test of UCRS e-learning DVD Usability Tst of UCRS -Larning DVD Dcmbr 2006 Writtn by Thrsa Wilkinson Excutiv Summary Usability Tst of th UCRS -Larning DVD, Dcmbr 2006 This rport documnts th findings of a usability tst of th UCRS -Larning

More information

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) Intrnational Association of Scintific Innovation and Rsarch (IASIR) (An Association Unifing th Scincs, Enginring, and Applid Rsarch) ISSN (Print): 79-000 ISSN (Onlin): 79-009 Intrnational Journal of Enginring,

More information

Teaching Computer Networking with the Help of Personal Computer Networks

Teaching Computer Networking with the Help of Personal Computer Networks Taching Computr Ntworking with th Hlp of Prsonal Computr Ntworks Rocky K. C. Chang Dpartmnt of Computing Th Hong Kong Polytchnic Univrsity Hung Hom, Kowloon, Hong Kong csrchang@comp.polyu.du.hk ABSTRACT

More information

GOAL SETTING AND PERSONAL MISSION STATEMENT

GOAL SETTING AND PERSONAL MISSION STATEMENT Prsonal Dvlopmnt Track Sction 4 GOAL SETTING AND PERSONAL MISSION STATEMENT Ky Points 1 Dfining a Vision 2 Writing a Prsonal Mission Statmnt 3 Writing SMART Goals to Support a Vision and Mission If you

More information

Whole Systems Approach to CO 2 Capture, Transport and Storage

Whole Systems Approach to CO 2 Capture, Transport and Storage Whol Systms Approach to CO 2 Captur, Transport and Storag N. Mac Dowll, A. Alhajaj, N. Elahi, Y. Zhao, N. Samsatli and N. Shah UKCCS Mting, July 14th 2011, Nottingham, UK Ovrviw 1 Introduction 2 3 4 Powr

More information

Version 1.0. General Certificate of Education (A-level) January 2012. Mathematics MPC3. (Specification 6360) Pure Core 3. Final.

Version 1.0. General Certificate of Education (A-level) January 2012. Mathematics MPC3. (Specification 6360) Pure Core 3. Final. Vrsion.0 Gnral Crtificat of Education (A-lvl) January 0 Mathmatics MPC (Spcification 660) Pur Cor Final Mark Schm Mark schms ar prpard by th Principal Eaminr and considrd, togthr with th rlvant qustions,

More information

The Constrained Ski-Rental Problem and its Application to Online Cloud Cost Optimization

The Constrained Ski-Rental Problem and its Application to Online Cloud Cost Optimization 3 Procdings IEEE INFOCOM Th Constraind Ski-Rntal Problm and its Application to Onlin Cloud Cost Optimization Ali Khanafr, Murali Kodialam, and Krishna P. N. Puttaswam Coordinatd Scinc Laborator, Univrsit

More information

Asset set Liability Management for

Asset set Liability Management for KSD -larning and rfrnc products for th global financ profssional Highlights Library of 29 Courss Availabl Products Upcoming Products Rply Form Asst st Liability Managmnt for Insuranc Companis A comprhnsiv

More information

Free ACA SOLUTION (IRS 1094&1095 Reporting)

Free ACA SOLUTION (IRS 1094&1095 Reporting) Fr ACA SOLUTION (IRS 1094&1095 Rporting) Th Insuranc Exchang (301) 279-1062 ACA Srvics Transmit IRS Form 1094 -C for mployrs Print & mail IRS Form 1095-C to mploys HR Assist 360 will gnrat th 1095 s for

More information

TIME MANAGEMENT. 1 The Process for Effective Time Management 2 Barriers to Time Management 3 SMART Goals 4 The POWER Model e. Section 1.

TIME MANAGEMENT. 1 The Process for Effective Time Management 2 Barriers to Time Management 3 SMART Goals 4 The POWER Model e. Section 1. Prsonal Dvlopmnt Track Sction 1 TIME MANAGEMENT Ky Points 1 Th Procss for Effctiv Tim Managmnt 2 Barrirs to Tim Managmnt 3 SMART Goals 4 Th POWER Modl In th Army, w spak of rsourcs in trms of th thr M

More information

Global Sourcing: lessons from lean companies to improve supply chain performances

Global Sourcing: lessons from lean companies to improve supply chain performances 3 rd Intrnational Confrnc on Industrial Enginring and Industrial Managmnt XIII Congrso d Ingniría d Organización Barclona-Trrassa, Sptmbr 2nd-4th 2009 Global Sourcing: lssons from lan companis to improv

More information

FEASIBILITY STUDY OF JUST IN TIME INVENTORY MANAGEMENT ON CONSTRUCTION PROJECT

FEASIBILITY STUDY OF JUST IN TIME INVENTORY MANAGEMENT ON CONSTRUCTION PROJECT FEASIBILITY STUDY OF JUST IN TIME INVENTORY MANAGEMENT ON CONSTRUCTION PROJECT Patil Yogndra R. 1, Patil Dhananjay S. 2 1P.G.Scholar, Dpartmnt of Civil Enginring, Rajarambapu Institut of Tchnology, Islampur,

More information

A Note on Approximating. the Normal Distribution Function

A Note on Approximating. the Normal Distribution Function Applid Mathmatical Scincs, Vol, 00, no 9, 45-49 A Not on Approimating th Normal Distribution Function K M Aludaat and M T Alodat Dpartmnt of Statistics Yarmouk Univrsity, Jordan Aludaatkm@hotmailcom and

More information

Online Price Competition within and between Heterogeneous Retailer Groups

Online Price Competition within and between Heterogeneous Retailer Groups Onlin Pric Comptition within and btwn Htrognous Rtailr Groups Cnk Kocas Dpartmnt of Markting and Supply Chain Managmnt, Michigan Stat Univrsity kocas@msu.du Abstract This study prsnts a modl of pric comptition

More information

SCHOOLS' PPP : PROJECT MANAGEMENT

SCHOOLS' PPP : PROJECT MANAGEMENT Rport Schools' PPP Sub Committ 22 April 2004 2 SCHOOLS' PPP : PROJECT MANAGEMENT 1 Rason for Rport To provid Mmbrs with information on th structur of th Schools' PPP Projct Tam 2 Background 21 Dumfris

More information

UNIVERSITY OF NAIROBI SCHOOL OF COMPUTING & INFORMATICS IMPROVING APPLICATION OF KNOWLEDGE MANAGEMENT SYSTEMS IN ORGANIZATIONS:

UNIVERSITY OF NAIROBI SCHOOL OF COMPUTING & INFORMATICS IMPROVING APPLICATION OF KNOWLEDGE MANAGEMENT SYSTEMS IN ORGANIZATIONS: UNIVERSITY OF NAIROBI SCHOOL OF COMPUTING & INFORMATICS IMPROVING APPLICATION OF KNOWLEDGE MANAGEMENT SYSTEMS IN ORGANIZATIONS: CASE OF NAIROBI CITY WATER AND SEWERAGE COMPANY By TABITHA MBETE NGEI P58/63441/2011

More information

Designing a Secure DNS Architecture

Designing a Secure DNS Architecture WHITE PAPER Dsigning a Scur DNS Architctur In today s ntworking landscap, it is no longr adquat to hav a DNS infrastructur that simply rsponds to quris. What is ndd is an intgratd scur DNS architctur that

More information

CalOHI Content Management System Review

CalOHI Content Management System Review CalOHI Contnt Systm Rviw Tabl of Contnts Documnt Ovrviw... 3 DotNtNuk... 4 Ovrviw... 4 Installation / Maintnanc... 4 Documntation... 5 Usability... 5 Dvlopmnt... 5 Ovrall... 6 CMS Mad Simpl... 6 Ovrviw...

More information

Performance Evaluation

Performance Evaluation Prformanc Evaluation ( ) Contnts lists availabl at ScincDirct Prformanc Evaluation journal hompag: www.lsvir.com/locat/pva Modling Bay-lik rputation systms: Analysis, charactrization and insuranc mchanism

More information

An Adaptive Clustering MAP Algorithm to Filter Speckle in Multilook SAR Images

An Adaptive Clustering MAP Algorithm to Filter Speckle in Multilook SAR Images An Adaptiv Clustring MAP Algorithm to Filtr Spckl in Multilook SAR Imags FÁTIMA N. S. MEDEIROS 1,3 NELSON D. A. MASCARENHAS LUCIANO DA F. COSTA 1 1 Cybrntic Vision Group IFSC -Univrsity of São Paulo Caia

More information

Traffic Flow Analysis (2)

Traffic Flow Analysis (2) Traffic Flow Analysis () Statistical Proprtis. Flow rat distributions. Hadway distributions. Spd distributions by Dr. Gang-Ln Chang, Profssor Dirctor of Traffic safty and Oprations Lab. Univrsity of Maryland,

More information

a m e s y s AMESYS INTELLIGENCE SOLUTIONS C RITIC A L SYSTEM ARCHITEC T www.amesys.fr SERVICES PROVIDED C O N T A C T S

a m e s y s AMESYS INTELLIGENCE SOLUTIONS C RITIC A L SYSTEM ARCHITEC T www.amesys.fr SERVICES PROVIDED C O N T A C T S SERVICES PROVIDED Smooth and profssional dploymnt With its growing xprinc, Amsys has mastrd th art of implmntation of intllignc systm. Our tam of profssional xprt will hlp you from th dsign of th architctur

More information