Yannis Kotidis - Academia.edu (original) (raw)
Papers by Yannis Kotidis
International Journal of Digital Curation, Jun 29, 2009
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Springer eBooks, 2012
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Springer eBooks, 2009
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Springer eBooks, 2009
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Proceedings of the VLDB Endowment, Apr 1, 2023
Approximating series of timestamped data points using a sequence of line segments with a maximum ... more Approximating series of timestamped data points using a sequence of line segments with a maximum error guarantee is a fundamental data compression problem, termed as piecewise linear approximation (PLA). Due to the increasing need to analyze massive collections of time-series data in diverse domains, the problem has recently received significant attention, and recent PLA algorithms that have emerged do help us handle the overwhelming amount of information, at the cost of some precision loss. More specifically, these algorithms entail a trade-off between the maximum precision loss and the space savings achieved. However, advances in the area of lossless compression are undercutting the offerings of PLA techniques in real datasets. In this work, we propose Sim-Piece, a novel lossy compression algorithm for time-series data that optimizes the space requirements of representing PLA line segments, by finding the minimum number of groups we can organize these segments into, to represent them jointly. Our experimental evaluation demonstrates that our approach readily outperforms competing techniques, attaining compression ratios with more than twofold improvement on average over what PLA algorithms can offer. This allows for providing significantly higher accuracy with equivalent space requirements. Moreover, our algorithm, due to the simplicity of its merging phase, imposes little overhead while compacting the PLA description, offering a significantly improved trade-off between space and running time. The aforementioned benefits of our approach significantly improve the efficiency in which we can store time-series data, while allowing a tight maximum error in the representation of their values.
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arXiv (Cornell University), Jul 18, 2016
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Data Engineering for Wireless and Mobile Access, Jun 29, 2009
It is our great pleasure to welcome you all to the Ninth ACM International Workshop on Data Engin... more It is our great pleasure to welcome you all to the Ninth ACM International Workshop on Data Engineering for Wireless and Mobile Access (MobiDE'10), held in conjunction with SIGMOD 2010. MobiDE continues its tradition of bringing together researchers and practitioners in databases, mobile computing, and networking, and providing a full day of exciting presentations and discussions. As in previous years, the workshop serves as a forum to present latest research and engineering results and contributions, and set future directions in wireless and mobile data management. MobiDE'10 is the ninth of a successful series of workshops that aims to act as a bridge between the data management, wireless networking, and mobile computing communities. The 1st MobiDE workshop took place in Seattle, USA (August 1999), in conjunction with MobiCom 1999. The 2nd MobiDE workshop was held in Santa Barbara, USA (May 2001), together with SIGMOD 2001. The 3rd MobiDE workshop was organized in San Diego, USA (September 2003), co-located with MobiCom 2003. The 4th, 5th, 6th, 7th, and 8th MobiDE workshops took place in Baltimore, USA (June 2005), Chicago, USA (June 2006), Beijing, China (June 2007), Vancouver, Canada (June 2008), and Providence, USA (June 2009), respectively, co-located with SIGMOD.
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Data Engineering for Wireless and Mobile Access, Jun 13, 2008
It is our great pleasure to welcome you all to the ACM International Workshop on Data Engineering... more It is our great pleasure to welcome you all to the ACM International Workshop on Data Engineering for Wireless and Mobile Access (MobiDE'08), held in conjunction with SIGMOD 2008. MobiDE continues its tradition of bringing together researchers and practitioners in databases, mobile computing, and networking, and providing a full day of exciting presentations and discussions. As in previous years, the workshop will serve as a forum to present latest research and engineering results and contributions, and set future directions in wireless and mobile data management. MobiDE'08 is the seventh of a series of workshops that strives to bridge the data management and mobile computing communities. The first MobiDE workshop (MobiDE'99) took place in Seattle in August 1999, in conjunction with MobiCom 1999. The second MobiDE workshop (MobiDE'01) was held in conjunction with SIGMOD/PODS 2001 in Santa Barbara in May 2001. The third MobiDE workshop was held in conjunction with MobiCom 2003 in San Diego in September 2003. Since the fourth edition of the workshop, MobiDE has been held in conjunction with SIGMOD/PODS and has been taken place on an annual basis. In 2005, MobiDE was held in Baltimore, Maryland. In 2006, MobiDE was held in Chicago, Illinois. Finally, in 2007, MobiDE was held in Beijing, China, also in conjunction with SIGMOD/PODS 2007. The call for papers for MobiDE'08 attracted 31 high-quality submissions, making the selection process very competitive. All papers were reviewed by three members of the Program Committee. Eventually, 9 papers were selected, resulting in an acceptance rate of 29%. The final program covers a broad variety of topics, including querying and security in mobile systems and applications, caching and replication, location-based data management, wireless sensor networks, communication and pervasive systems. We believe that these proceedings will thus serve as a valuable reference point for the latest results on mobile and wireless data engineering. In addition, the workshop program includes a keynote speech by Prof. Vassilis Tsotras of the University of California, Riverside. Several people contributed to the successful organization of MobiDE08. We thank the authors for providing the content of the program. We owe our sincere gratitude to the members of the technical Program Committee and external reviewers for their excellent work in reviewing the papers and providing valuable feedback under a tight deadline. We also thank Microsoft for granting us permission to use the Microsoft CMT service and the entire CMT support team, for their help in setting up and managing the online review process. Our special thanks go to the publicity chair, Demetris Zeinalipour, from the School of Pure and Applied Sciences, Open University of Cyprus and our Steering Committee members Ugur Cetintemel, from Brown University, Panos K. Chrysanthis, from the University of Pittsburgh, Christian Jensen, from Aalborg University, Alexandros Labrinidis, from the University of Pittsburgh, Dik Lun LEE, from HKUST and George Samaras, from the University of Cyprus. We are also grateful to our industrial sponsors: IBM, Microsoft Research and Microsoft .NET club for the financial support they provided. Last, but definitely not least, we want to thank ACM and, in particular SIGMOD, for sponsoring the workshop and SIGMOBILE for supporting it (MobiDE08 is sponsored by ACM SIGMOD and held in-cooperation with ACM SIGMOBILE). We acknowledge the help of many people from these organizations. First of all, we would like to thank Marianne Winslett, the SIGMOD Vice Chair, whose constant help, support, and guidance throughout the entire process were crucial to the success of MobiDE08. Special thanks also go to Erin Dolan, Adrienne Gristi, and Maritza Nichols from the ACM Headquarters, to Yannis Ioannidis, the SIGMOD Workshops Coordinator, to Carson Leung, Local Workshop Chair for SIGMOD/PODS 2008, and to Jian Pei, the SIGMOD Finance Chair, for their invaluable help with the budget process. Finally, we would also like to thank Laks V.S. Lakshmanan and Raymond T. Ng, the Chairs of SIGMOD, and David B. Johnson, the Chair of SIGMOBILE, for their support and help in making the MobiDE08 organization a success.
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Lecture Notes in Computer Science, 2016
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IEEE Transactions on Knowledge and Data Engineering, 2017
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Proceedings of the VLDB Endowment
Applications in diverse domains such as astronomy, economics and industrial monitoring, increasin... more Applications in diverse domains such as astronomy, economics and industrial monitoring, increasingly press the need for analyzing massive collections of time series data. The sheer size of the latter hinders our ability to efficiently store them and also yields significant storage costs. Applying general purpose compression algorithms would effectively reduce the size of the data, at the expense of introducing significant computational overhead. Time Series Management Systems that have emerged to address the challenge of handling this overwhelming amount of information, cannot suffer the ingestion rate restrictions that such compression algorithms would cause. Data points are usually encoded using faster, streaming compression approaches. However, the techniques that contemporary systems use do not fully utilize the compression potential of time series data, with implications in both storage requirements and access times. In this work, we propose a novel streaming compression algori...
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International Journal of Digital Curation, Jun 29, 2009
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Springer eBooks, 2012
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Springer eBooks, 2009
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Springer eBooks, 2009
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
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Proceedings of the VLDB Endowment, Apr 1, 2023
Approximating series of timestamped data points using a sequence of line segments with a maximum ... more Approximating series of timestamped data points using a sequence of line segments with a maximum error guarantee is a fundamental data compression problem, termed as piecewise linear approximation (PLA). Due to the increasing need to analyze massive collections of time-series data in diverse domains, the problem has recently received significant attention, and recent PLA algorithms that have emerged do help us handle the overwhelming amount of information, at the cost of some precision loss. More specifically, these algorithms entail a trade-off between the maximum precision loss and the space savings achieved. However, advances in the area of lossless compression are undercutting the offerings of PLA techniques in real datasets. In this work, we propose Sim-Piece, a novel lossy compression algorithm for time-series data that optimizes the space requirements of representing PLA line segments, by finding the minimum number of groups we can organize these segments into, to represent them jointly. Our experimental evaluation demonstrates that our approach readily outperforms competing techniques, attaining compression ratios with more than twofold improvement on average over what PLA algorithms can offer. This allows for providing significantly higher accuracy with equivalent space requirements. Moreover, our algorithm, due to the simplicity of its merging phase, imposes little overhead while compacting the PLA description, offering a significantly improved trade-off between space and running time. The aforementioned benefits of our approach significantly improve the efficiency in which we can store time-series data, while allowing a tight maximum error in the representation of their values.
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
arXiv (Cornell University), Jul 18, 2016
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Data Engineering for Wireless and Mobile Access, Jun 29, 2009
It is our great pleasure to welcome you all to the Ninth ACM International Workshop on Data Engin... more It is our great pleasure to welcome you all to the Ninth ACM International Workshop on Data Engineering for Wireless and Mobile Access (MobiDE'10), held in conjunction with SIGMOD 2010. MobiDE continues its tradition of bringing together researchers and practitioners in databases, mobile computing, and networking, and providing a full day of exciting presentations and discussions. As in previous years, the workshop serves as a forum to present latest research and engineering results and contributions, and set future directions in wireless and mobile data management. MobiDE'10 is the ninth of a successful series of workshops that aims to act as a bridge between the data management, wireless networking, and mobile computing communities. The 1st MobiDE workshop took place in Seattle, USA (August 1999), in conjunction with MobiCom 1999. The 2nd MobiDE workshop was held in Santa Barbara, USA (May 2001), together with SIGMOD 2001. The 3rd MobiDE workshop was organized in San Diego, USA (September 2003), co-located with MobiCom 2003. The 4th, 5th, 6th, 7th, and 8th MobiDE workshops took place in Baltimore, USA (June 2005), Chicago, USA (June 2006), Beijing, China (June 2007), Vancouver, Canada (June 2008), and Providence, USA (June 2009), respectively, co-located with SIGMOD.
Bookmarks Related papers MentionsView impact
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Data Engineering for Wireless and Mobile Access, Jun 13, 2008
It is our great pleasure to welcome you all to the ACM International Workshop on Data Engineering... more It is our great pleasure to welcome you all to the ACM International Workshop on Data Engineering for Wireless and Mobile Access (MobiDE'08), held in conjunction with SIGMOD 2008. MobiDE continues its tradition of bringing together researchers and practitioners in databases, mobile computing, and networking, and providing a full day of exciting presentations and discussions. As in previous years, the workshop will serve as a forum to present latest research and engineering results and contributions, and set future directions in wireless and mobile data management. MobiDE'08 is the seventh of a series of workshops that strives to bridge the data management and mobile computing communities. The first MobiDE workshop (MobiDE'99) took place in Seattle in August 1999, in conjunction with MobiCom 1999. The second MobiDE workshop (MobiDE'01) was held in conjunction with SIGMOD/PODS 2001 in Santa Barbara in May 2001. The third MobiDE workshop was held in conjunction with MobiCom 2003 in San Diego in September 2003. Since the fourth edition of the workshop, MobiDE has been held in conjunction with SIGMOD/PODS and has been taken place on an annual basis. In 2005, MobiDE was held in Baltimore, Maryland. In 2006, MobiDE was held in Chicago, Illinois. Finally, in 2007, MobiDE was held in Beijing, China, also in conjunction with SIGMOD/PODS 2007. The call for papers for MobiDE'08 attracted 31 high-quality submissions, making the selection process very competitive. All papers were reviewed by three members of the Program Committee. Eventually, 9 papers were selected, resulting in an acceptance rate of 29%. The final program covers a broad variety of topics, including querying and security in mobile systems and applications, caching and replication, location-based data management, wireless sensor networks, communication and pervasive systems. We believe that these proceedings will thus serve as a valuable reference point for the latest results on mobile and wireless data engineering. In addition, the workshop program includes a keynote speech by Prof. Vassilis Tsotras of the University of California, Riverside. Several people contributed to the successful organization of MobiDE08. We thank the authors for providing the content of the program. We owe our sincere gratitude to the members of the technical Program Committee and external reviewers for their excellent work in reviewing the papers and providing valuable feedback under a tight deadline. We also thank Microsoft for granting us permission to use the Microsoft CMT service and the entire CMT support team, for their help in setting up and managing the online review process. Our special thanks go to the publicity chair, Demetris Zeinalipour, from the School of Pure and Applied Sciences, Open University of Cyprus and our Steering Committee members Ugur Cetintemel, from Brown University, Panos K. Chrysanthis, from the University of Pittsburgh, Christian Jensen, from Aalborg University, Alexandros Labrinidis, from the University of Pittsburgh, Dik Lun LEE, from HKUST and George Samaras, from the University of Cyprus. We are also grateful to our industrial sponsors: IBM, Microsoft Research and Microsoft .NET club for the financial support they provided. Last, but definitely not least, we want to thank ACM and, in particular SIGMOD, for sponsoring the workshop and SIGMOBILE for supporting it (MobiDE08 is sponsored by ACM SIGMOD and held in-cooperation with ACM SIGMOBILE). We acknowledge the help of many people from these organizations. First of all, we would like to thank Marianne Winslett, the SIGMOD Vice Chair, whose constant help, support, and guidance throughout the entire process were crucial to the success of MobiDE08. Special thanks also go to Erin Dolan, Adrienne Gristi, and Maritza Nichols from the ACM Headquarters, to Yannis Ioannidis, the SIGMOD Workshops Coordinator, to Carson Leung, Local Workshop Chair for SIGMOD/PODS 2008, and to Jian Pei, the SIGMOD Finance Chair, for their invaluable help with the budget process. Finally, we would also like to thank Laks V.S. Lakshmanan and Raymond T. Ng, the Chairs of SIGMOD, and David B. Johnson, the Chair of SIGMOBILE, for their support and help in making the MobiDE08 organization a success.
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Lecture Notes in Computer Science, 2016
Bookmarks Related papers MentionsView impact
IEEE Transactions on Knowledge and Data Engineering, 2017
Bookmarks Related papers MentionsView impact
Proceedings of the VLDB Endowment
Applications in diverse domains such as astronomy, economics and industrial monitoring, increasin... more Applications in diverse domains such as astronomy, economics and industrial monitoring, increasingly press the need for analyzing massive collections of time series data. The sheer size of the latter hinders our ability to efficiently store them and also yields significant storage costs. Applying general purpose compression algorithms would effectively reduce the size of the data, at the expense of introducing significant computational overhead. Time Series Management Systems that have emerged to address the challenge of handling this overwhelming amount of information, cannot suffer the ingestion rate restrictions that such compression algorithms would cause. Data points are usually encoded using faster, streaming compression approaches. However, the techniques that contemporary systems use do not fully utilize the compression potential of time series data, with implications in both storage requirements and access times. In this work, we propose a novel streaming compression algori...
Bookmarks Related papers MentionsView impact
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