Browsing by Author "Çetintemel, U."

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    A comparison of epidemic algorithms in wireless sensor networks
    (Elsevier BV, 2006-08-21) Akdere, M.; Bilgin, C. C.; Gerdaneri, O.; Korpeoglu, I.; Ulusoy, O.; Çetintemel, U.
    We consider the problem of reliable data dissemination in the context of wireless sensor networks. For some application scenarios, reliable data dissemination to all nodes is necessary for propagating code updates, queries, and other sensitive information in wireless sensor networks. Epidemic algorithms are a natural approach for reliable distribution of information in such ad hoc, decentralized, and dynamic environments. In this paper we show the applicability of epidemic algorithms in the context of wireless sensor environments, and provide a comparative performance analysis of the three variants of epidemic algorithms in terms of message delivery rate, average message latency, and messaging overhead on the network. © 2006 Elsevier B.V. All rights reserved.
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    OBJECTIVE: A benchmark for object-oriented active database systems
    (Elsevier, 1999) Çetintemel, U.; Zimmermann, J.; Ulusoy, Özgür; Buchmann, A.
    Although much work in the area of Active Database Management Systems (ADBMSs) has been done, it is not yet clear how the performance of an active DBMS can be evaluated systematically. In this paper, we describe the OBJECTIVE Benchmark for object-oriented ADBMSs, and present experimental results from its implementation in an active database system prototype. OBJECTIVE can be used to identify performance bottlenecks and active functionalities of an ADBMS, and to compare the performance of multiple ADBMSs.
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    Towards interactive data exploration
    (Springer, 2019) Binnig, C.; Basık, Fuat; Buratti, B.; Çetintemel, U.; Chung, Y.; Crotty, A.; Cousins, C.; Ebert, D.; Eichmann, P.; Galakatos, A.; Hattasch, B.; Ilkhechi, A.; Kraska, T.; Shang, Z.; Tromba, I.; Usta, Arif; Utama, P.; Upfal, E.; Wang, L.; Weir, N.; Zeleznik, R.; Zgraggen, E.; Castellanos, M.; Chrysanthis, P.; Pelechrinis, K.
    Enabling interactive visualization over new datasets at “human speed” is key to democratizing data science and maximizing human productivity. In this work, we first argue why existing analytics infrastructures do not support interactive data exploration and outline the challenges and opportunities of building a system specifically designed for interactive data exploration. Furthermore, we present the results of building IDEA, a new type of system for interactive data exploration that is specifically designed to integrate seamlessly with existing data management landscapes and allow users to explore their data instantly without expensive data preparation costs. Finally, we discuss other important considerations for interactive data exploration systems including benchmarking, natural language interfaces, as well as interactive machine learning.