irina rish dwtcusibmcom apologies multiple postings ecmlpkdd workshop autonomic computing new challenge machine learning september 22 2006 berlin germany httpdominoresearchibmcomcommresearch_peoplensfpagesrishindexhtmlfileecml_index1 html workshop autonomic computing new challenge machine learning held september 22 2006 conjunction 17th european conference machine learning 10th european conference principles practice knowledge discovery databases berlin germany workshop description combined tutorial workshop explore many new challenges machine learning arise context emerging field autonomic computing largescale distributed computing systems evolving multivendor tangles heterogeneous components may hundreds configuration tuning parameters rapidly becoming difficult humans configure tune maintain goal autonomic computing avert looming complexity crisis giving computing systems components ability manage accordance highlevel objectives fromadministrators machine learning appears promising approach challenges could replace laborious time consuming often suboptimal handcrafted rules models typically used management tasks stateofart systems adaptive online learning decisionmaking methods example reinforcement learning another hope adaptive online decisionmaking via ml approaches cope better complex nonstationary dynamic system behavior explicit control queuing models used advanced systems management today moreover manual selection relevant metrics measurements essential effective management could replaces dimensionality reduction active learning approaches would help squeeze minimal cost mostrelevant systems applications necessarily mean data collected informative respect question interest relevant decision needs made indeed drowning data starving knowledge good news systems management domain naturally suited active learning exploration vs exploitation reinforcement learning since various measurements tests constructed performed demand feature always present natural domains biology medicine tests costly constrained tutorial plan cover indepth existing machinelearning approaches various management tasks reinforcement learning rl particularly hybrid rl combines experts knowledge learning data active learning costsensitive learning exploration vs exploitation tradeoffs decisionmaking dimensionality reduction feature selection complex networks properties algorithms examples applying approaches various systems management tasks including resource allocation diagnosis workshop following tutorial hope bring together academic industrial researchers share experience applying machine learning complex distributed systems management identify new challenges machinelearning community match mature technologies current problems chart trajectory interdisciplinary research techniques applied autonomic computing also hope build workshop upon success two recent nips workshops nips05 workshop value information httpdominoresearchibmcomcommresearch_projectsnsfpagesnips05workshopindexhtml relates active learning feature selection exploration vs exploitation rl well nips03 workshop complex networks httpwwwresearchibmcomnips03workshop relates various issues todays actively researched area artificial internet web natural biological social etc complex networks distributed systems also hope build upon sysml06 workshop httpresearchmicrosoftcomworkshopssysml organized conjunction acm sigmetrics conference year one organizers gerry teasuro pc committee topics topics interest include limited applications machine learning distributed systems networks various kinds including internet applications peertopeer systems grid computing sensor networks new challenging problems machine learning arise systems management areas security reliability performance online learning methods handling large volumes realtime data eg event streams scalability issues feature selection dimensionality reduction clustering techniques filtering large amounts systems data adaptive decisionmaking approaches implement selfmanagement capabilities eg resource allocation novel machine learning techniques successfully applied systems management examples realworld systems benefit machinelearning approaches paper submission invite submissions technical papers 12 pages maximum short position papers andor work progress 6 pages maximum papers selected presentation andor publication workshop proceedings based originality technical merit topical relevance likelihood generating discussion workshop submitted papers reviewed least two members program committee submissions made electronically email attachment preferably postscript pdf format submissions must sent rishusibmcom although required initial submission recommend follow format guidelines ecmlpkdd springer lncs latex style file available httpwwwspringerdecomplncsauthorshtml required format accepted papers workshop proceedings available online hard copies distributed workshop important dates submission deadline july 3 2006 submit papers rishusibmcom notification acceptance july 24 2006 cameraready copies due august 14 2006 submit papers rishusibmcom workshop september 22 2006 workshop program workshop schedule announced later organizers irina rish ibm tj watson research center 19 skyline drive hawthorne ny 10532 email dwtcusibmcom rajarshi das ibm tj watson research center 19 skyline drive hawthorne ny 10532 email vkpkhlncusibmcom gerald tesauro ibm tj watson research center 19 skyline drive hawthorne ny 10532 email ecfpfshkusibmcom jeff kephart ibm tj watson research center 19 skyline drive hawthorne ny 10532 email xxaoksbusibmcom thu 07 aug 2008 022509 0400 uai ecmlpkdd06 workshop autonomic computing new challenge machine learning