無線センサネットワークにおける連続データ収集のための分散コーディング手法に関する研究

筑波大学 (University of Tsukuba)201

Advanced methods for query routing in peer-to-peer information retrieval

One of the most challenging problems in peer-to-peer networks is query routing: effectively and efficiently identifying peers that can return high-quality local results for a given query. Existing methods from the areas of distributed information retrieval and metasearch engines do not adequately address the peculiarities of a peer-to-peer network. The main contributions of this thesis are as follows: 1. Methods for query routing that take into account the mutual overlap of different peers\u27; collections, 2. Methods for query routing that take into account the correlations between multiple terms, 3. Comparative evaluation of different query routing methods. Our experiments confirm the superiority of our novel query routing methods over the prior state-of-the-art, in particular in the context of peer-to-peer Web search.Eines der drängendsten Probleme in Peer-to-Peer-Netzwerken ist Query-Routing: das effektive und effiziente Identifizieren solcher Peers, die qualitativ hochwertige lokale Ergebnisse zu einer gegebenen Anfrage liefern können. Die bisher bekannten Verfahren aus dem Bereich der verteilten Informationssuche sowie der Metasuchmaschinen werden den Besonderheiten von Peer-to-Peer-Netzwerken nicht gerecht. Die Hautbeiträge dieser Arbeit teilen sich in folgende Schwerpunkte: 1. Query-Routing unter Berücksichtigung der gegenseitigen überlappung der Kollektionen verschiedener Peers, 2. Query-Routing unter Berücksichtigung der Korrelationen zwischen verschiedenen Termen, 3. Vergleichende Evaluierung verschiedener Methoden zum Query-Routing. Unsere Experimente bestätigen die Überlegenheit der in dieser Arbeit entwickelten Verfahren gegenüber den bisher bekannten Verfahren, insbesondere im Kontext von Peer-to-Peer-Websuche

Äriprotsessi tulemuste ennustav ja korralduslik seire

Viimastel aastatel on erinevates valdkondades tegutsevad ettevõtted üles näidanud kasvavat huvi masinõppel põhinevate rakenduste kasutusele võtmiseks. Muuhulgas otsitakse võimalusi oma äriprotsesside efektiivsuse tõstmiseks, kasutades ennustusmudeleid protsesside jooksvaks seireks. Sellised ennustava protsessiseire meetodid võtavad sisendiks sündmuslogi, mis koosneb hulgast lõpetatud äriprotsessi juhtumite sündmusjadadest, ning kasutavad masinõppe algoritme ennustusmudelite treenimiseks. Saadud mudelid teevad ennustusi lõpetamata (antud ajahetkel aktiivsete) protsessijuhtumite jaoks, võttes sisendiks sündmuste jada, mis selle hetkeni on toimunud ning ennustades kas järgmist sündmust antud juhtumis, juhtumi lõppemiseni jäänud aega või instantsi lõpptulemust. Lõpptulemusele orienteeritud ennustava protsessiseire meetodid keskenduvad ennustamisele, kas protsessijuhtum lõppeb soovitud või ebasoovitava lõpptulemusega. Süsteemi kasutaja saab ennustuste alusel otsustada, kas sekkuda antud protsessijuhtumisse või mitte, eesmärgiga ära hoida ebasoovitavat lõpptulemust või leevendada selle negatiivseid tagajärgi. Erinevalt puhtalt ennustavatest süsteemidest annavad korralduslikud protsessiseire meetodid kasutajale ka soovitusi, kas ja kuidas antud juhtumisse sekkuda, eesmärgiga optimeerida mingit kindlat kasulikkusfunktsiooni. Käesolev doktoritöö uurib, kuidas treenida, hinnata ja kasutada ennustusmudeleid äriprotsesside lõpptulemuste ennustava ja korraldusliku seire raames. Doktoritöö pakub välja taksonoomia olemasolevate meetodite klassifitseerimiseks ja võrdleb neid katseliselt. Lisaks pakub töö välja raamistiku tekstiliste andmete kasutamiseks antud ennustusmudelites. Samuti pakume välja ennustuste ajalise stabiilsuse mõiste ning koostame raamistiku korralduslikuks protsessiseireks, mis annab kasutajatele soovitusi, kas protsessi sekkuda või mitte. Katsed näitavad, et väljapakutud lahendused täiendavad olemasolevaid meetodeid ning aitavad kaasa ennustava protsessiseire süsteemide rakendamisele reaalsetes süsteemides.Recent years have witnessed a growing adoption of machine learning techniques for business improvement across various fields. Among other emerging applications, organizations are exploiting opportunities to improve the performance of their business processes by using predictive models for runtime monitoring. Such predictive process monitoring techniques take an event log (a set of completed business process execution traces) as input and use machine learning techniques to train predictive models. At runtime, these techniques predict either the next event, the remaining time, or the final outcome of an ongoing case, given its incomplete execution trace consisting of the events performed up to the present moment in the given case. In particular, a family of techniques called outcome-oriented predictive process monitoring focuses on predicting whether a case will end with a desired or an undesired outcome. The user of the system can use the predictions to decide whether or not to intervene, with the purpose of preventing an undesired outcome or mitigating its negative effects. Prescriptive process monitoring systems go beyond purely predictive ones, by not only generating predictions but also advising the user if and how to intervene in a running case in order to optimize a given utility function. This thesis addresses the question of how to train, evaluate, and use predictive models for predictive and prescriptive monitoring of business process outcomes. The thesis proposes a taxonomy and performs a comparative experimental evaluation of existing techniques in the field. Moreover, we propose a framework for incorporating textual data to predictive monitoring systems. We introduce the notion of temporal stability to evaluate these systems and propose a prescriptive process monitoring framework for advising users if and how to act upon the predictions. The results suggest that the proposed solutions complement the existing techniques and can be useful for practitioners in implementing predictive process monitoring systems in real life

Cost- and workload-driven data management in the cloud

This thesis deals with the challenge of finding the right balance between consistency, availability, latency and costs, captured by the CAP/PACELC trade-offs, in the context of distributed data management in the Cloud. At the core of this work, cost and workload-driven data management protocols, called CCQ protocols, are developed. First, this includes the development of C3, which is an adaptive consistency protocol that is able to adjust consistency at runtime by considering consistency and inconsistency costs. Second, the development of Cumulus, an adaptive data partitioning protocol, that can adapt partitions by considering the application workload so that expensive distributed transactions are minimized or avoided. And third, the development of QuAD, a quorum-based replication protocol, that constructs the quorums in such a way so that, given a set of constraints, the best possible performance is achieved. The behavior of each CCQ protocol is steered by a cost model, which aims at reducing the costs and overhead for providing the desired data management guarantees. The CCQ protocols are able to continuously assess their behavior, and if necessary to adapt the behavior at runtime based on application workload and the cost model. This property is crucial for applications deployed in the Cloud, as they are characterized by a highly dynamic workload, and high scalability and availability demands. The dynamic adaptation of the behavior at runtime does not come for free, and may generate considerable overhead that might outweigh the gain of adaptation. The CCQ cost models incorporate a control mechanism, which aims at avoiding expensive and unnecessary adaptations, which do not provide any benefits to applications. The adaptation is a distributed activity that requires coordination between the sites in a distributed database system. The CCQ protocols implement safe online adaptation approaches, which exploit the properties of 2PC and 2PL to ensure that all sites behave in accordance with the cost model, even in the presence of arbitrary failures. It is crucial to guarantee a globally consistent view of the behavior, as in contrary the effects of the cost models are nullified. The presented protocols are implemented as part of a prototypical database system. Their modular architecture allows for a seamless extension of the optimization capabilities at any level of their implementation. Finally, the protocols are quantitatively evaluated in a series of experiments executed in a real Cloud environment. The results show their feasibility and ability to reduce application costs, and to dynamically adjust the behavior at runtime without violating their correctness