Publications

249 entries « ‹ 3 of 5 › »

2016

Julia Stoyanovich, Serge Abiteboul, Gerome Miklau

Data Responsibly: Fairness, Neutrality and Transparency in Data Analysis Inproceedings

Proceedings of the 19th International Conference on Extending Database Technology, EDBT 2016, Bordeaux, France, March 15-16, 2016, Bordeaux, France, March 15-16, 2016., pp. 718–719, 2016.

Abstract | Links | BibTeX

Yanlei Diao, Michael J. Franklin

High-Performance XML Message Brokering Incollection

Data Stream Management - Processing High-Speed Data Streams, pp. 451–471, 2016.

Abstract | Links | BibTeX

@incollection{DBLP:books/sp/16/DiaoF16,
title = {High-Performance XML Message Brokering},
author = {Yanlei Diao and
Michael J. Franklin},
url = {http://dx.doi.org/10.1007/978-3-540-28608-0_22},
doi = {10.1007/978-3-540-28608-0_22},
year = {2016},
date = {2016-01-01},
booktitle = {Data Stream Management - Processing High-Speed Data Streams},
pages = {451--471},
crossref = {DBLP:books/sp/GGR2016},
abstract = {For distributed environments including Web Services, data and application integration, and personalized content delivery, XML is becoming the common wire format for data. In this emerging distributed infrastructure, XML message brokers will play a key role as central exchange points for messages sent between applications and/or users. Users (equivalently, applications, or organizations) subscribe to the message broker by providing profiles expressing their data interests. After arriving at the message broker, these profiles become “standing queries,” which are executed on all incoming data. Data sources publish their data by pushing streams of XML messages to the broker. The broker delivers to each user the messages that match his data interests; these messages are presented in the required format of the user. We have developed “YFilter”, an XML filtering system aimed at providing efficient filtering for large numbers (e.g., 10’s or 100’s of thousands) of path queries. The key innovation in YFilter is a Nondeterministic Finite Automaton (NFA)-based representation of path expressions which combines all queries into a single machine. YFilter exploits commonality among path queries by merging the common prefixes of the paths so that they are processed at most once. The NFA-based implementation also provides additional benefits including a relatively small machine size, flexibility in dealing with diverse characteristics of data and queries, incremental machine construction, and ease of maintenance.

This work has been supported in part by the National Science Foundation under the ITR grants IIS0086057 and SI0122599 and by Boeing, IBM, Intel, Microsoft, Siemens, and the UC MICRO program.},
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pubstate = {published},
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}

Kyriaki Dimitriadou, Olga Papaemmanouil, Yanlei Diao

AIDE: An Active Learning-Based Approach for Interactive Data Exploration Journal Article

IEEE Trans. Knowl. Data Eng., 28 (11), pp. 2842–2856, 2016.

Abstract | Links | BibTeX

Yue Wang, Alexandra Meliou, Gerome Miklau

Lifting the Haze off the Cloud: A Consumer-Centric Market for Database Computation in the Cloud Journal Article

PVLDB, 10 (4), pp. 373–384, 2016.

Abstract | Links | BibTeX

@article{DBLP:journals/pvldb/WangMM16,
title = {Lifting the Haze off the Cloud: A Consumer-Centric Market for Database Computation in the Cloud},
author = {Yue Wang and
Alexandra Meliou and
Gerome Miklau},
url = {http://www.vldb.org/pvldb/vol10/p373-wang.pdf},
year = {2016},
date = {2016-01-01},
journal = {PVLDB},
volume = {10},
number = {4},
pages = {373--384},
abstract = {The availability of public computing resources in the cloud has revolutionized data analysis, but requesting cloud resources often involves complex decisions for consumers. Estimating the completion time and cost of a computation and requesting the appropriate cloud resources are challenging tasks even for an expert user. We propose a new market-based framework for pricing computational tasks in the cloud. Our framework introduces an agent between consumers and cloud providers. The agent takes data and computational tasks from users, estimates time and cost for evaluating the tasks, and returns to consumers contracts that specify the price and completion time. Our framework can be applied directly to existing cloud markets without altering the way cloud providers offer and price services. In addition, it simplifies cloud use for consumers by allowing them to compare contracts, rather than choose resources directly. We present design, analytical, and algorithmic contributions focusing on pricing computation contracts, analyzing their properties, and optimizing them in complex workflows. We conduct an experimental evaluation of our market framework over a real-world cloud service and demonstrate empirically that our market ensures three key properties: (a) that consumers benefit from using the market due to competitiveness among agents, (b) that agents have an incentive to price contracts fairly, and (c) that inaccuracies in estimates do not pose a significant risk to agents' profits. Finally, we present a fine-grained pricing mechanism for complex workflows and show that it can increase agent profits by more than an order of magnitude in some cases.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Serge Abiteboul, Gerome Miklau, Julia Stoyanovich, Gerhard Weikum

Data, Responsibly (Dagstuhl Seminar 16291) Journal Article

Dagstuhl Reports, 6 (7), pp. 42–71, 2016.

Abstract | Links | BibTeX

Olga Papaemmanouil, Yanlei Diao, Kyriaki Dimitriadou, Liping Peng

Interactive Data Exploration via Machine Learning Models Journal Article

IEEE Data Eng. Bull., 39 (4), pp. 38–49, 2016.

Abstract | Links | BibTeX

2015

Yanlei Diao, Abhishek Roy, Toby Bloom

Building Highly-Optimized, Low-Latency Pipelines for Genomic Data Analysis Inproceedings

CIDR 2015, Seventh Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, January 4-7, 2015, Online Proceedings, 2015.

Publications

2016

2015

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