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of the 1999 SIGMOD paper: Computing multidimensional aggregates in high dimensions is a performance bottleneck for many OLAP applications. Obtaining the exact answer to an aggregation query can be prohibitively expensive in terms of time and/or storage space in a data warehouse environment. It is advantageous to have fast, approximate answers to OLAP aggregation queries. In this paper, we present a novel method that provides approximate answers to high-dimensional OLAP aggregation queries in massive sparse data sets in a time-efficient and space-efficient manner. We construct a compact data cube, which is an approximate and space-efficient representation of the underlying multidimensional array, based upon a multiresolution wavelet decomposition. In the on-line phase, each aggregation query can generally be answered using the compact data cube in one I/O or a small number of I/Os, depending upon the desired accuracy. We present two I/O-efficient algorithms to construct the compact data cube for the important case of sparse high-dimensional arrays, which often arise in practice. The traditional histogram methods are infeasible for the massive high-dimensional data sets in OLAP applications. Previously developed wavelet techniques are efficient only for dense data. Our online query processing algorithm is very fast and capable of refining answers as the user demands more accuracy. Experiments on real data show that our method provides significantly more accurate results for typical OLAP aggregation queries than other efficient approximation techniques such as random sampling. 6 SIGMOD Record, June 2009 (Vol. 38, No. 2) SIGMOD Best Paper Award 2009 SIGMOD Best Paper Award Generating Example Data for Dataflow Programs Christopher Olston, Shubham Chopra, Utkarsh Srivastava (Yahoo! Research) While developing data-centric programs, users often run (portions of) their programs over real data, to see how they behave and what the output looks like. Doing so makes it easier to formulate, understand and compose programs correctly, compared with examination of program logic alone. For large input data sets, these experimental runs can be time-consuming and inefficient. Unfortunately, sampling the input data does not always work well, because selective operations such as filter and join can lead to empty results over sampled inputs, and unless certain indexes are present there is no way to generate biased samples efficiently. Consequently new methods are needed for generating example input data for data-centric programs. We focus on an important category of data-centric programs, dataflow programs, which are best illustrated by displaying the series of intermediate data tables that occur between each pair of operations. We introduce and study the problem of generating example intermediate data for dataflow programs, in a manner that illustrates the semantics of the operators while keeping the example data small. We identify two major obstacles that impede naive approaches, namely (1) highly selective operators and (2) noninvertible operators, and offer techniques for dealing with these obstacles. Our techniques perform well on real dataflow programs used at Yahoo! for web analytics. http://doi.acm. org/10.1145/1559845.1559873 2009 SIGMOD Best Paper Award Runner-up An Architecture for Recycling Intermediates in a Column-store Milena G. Ivanova, Martin L. Kersten, Niels J. Nes, Romulo A.P. Goncalves (CWI) Automatically recycling (intermediate) results is a grand challenge for state-of-the-art databases to improve both query response time and throughput. Tuples are loaded and streamed through a tuple-at-a-time processing pipeline avoiding materialization of intermediates as much as possible. This limits the opportunities for reuse of overlapping computations to DBA-defined materialized views and function/result cache tuning. In contrast, the operator-at-a-time execution paradigm produces fully materialized results in each step of the query plan. To avoid resource contention, these intermediates are evicted as soon as possible. In this paper we study an architecture that harvests the by-products of the operator-at-a-time paradigm in a column store system using a lightweight mechanism, the recycler. The key challenge then becomes selection of the policies to admit intermediates to the resource pool, their retention period, and the eviction strategy when facing resource limitations. The proposed recycling architecture has been implemented in an open-source system. An experimental analysis against the TPC-H ad-hoc decision support benchmark and a complex, real-world application (SkyServer) demonstrates its effectiveness in terms of self-organizing behavior and its significant performance gains. The results indicate the potentials of recycling intermediates and charters a route for further development of database kernels. http://doi.acm. org/10.1145/1559845.1559879 SIGMOD Record, June 2009 (Vol. 38, No. 2) 7 SIGMOD Jim Gray Doctoral Dissertation Award SIGMOD has established the annual SIGMOD Jim Gray Doctoral Dissertation Award to recognize excellent research by doctoral candidates in the database field. This award, which was previously known as the SIGMOD Doctoral Dissertation Award, was renamed in 2008 with the unanimous approval of ACM Council in honor of Dr. Jim Gray. 2009 SIGMOD Jim Gray Doctoral Dissertation Award Winner • Daniel Abadi (advisor: Samuel Madden), MIT Dissertation: Query Execution in Column-Oriented Database Systems. This dissertation addresses all the key aspects of implementing an efficient database system based on a column-store architecture including storage management, compression strategies, query execution techniques, and alternative strategies for materializing result rows. The thesis is remarkable for its breadth, depth, system implementation and impact, and is an excellent treatise on the design and implementation of database systems employing a column-oriented storage model. The work is significant in bringing sound database design principles into practice and offering efficient solutions to urgent real problems. 2009 SIGMOD Jim Gray Doctoral Dissertation Award Honorable Mentions: • Bee-Chung Chen (advisor: Raghu Ramakrishnan), University of Wisconsin at Madison Dissertation: Cube-space Data Mining • Ashwin Machanavajjhala (advisor: Johannes Gehrke), Cornell University. Dissertation: Defining and Enforcing Privacy in Data Sharing The annual SIGMOD Jim Gray Dissertation Award recognizes excellent research by doctoral candidates in the database field. For the 2009 award a dissertation needed to be completed and accepted by the candidate's department between September 1, 2007 and December 15, 2008. This year 14 outstanding dissertations were nominated by their departments. The dissertations were evaluated by the SIGMOD Jim Gray Dissertation Awards Committee (Beng Chin Ooi (cochair), Johannes Gehkre (co-chair---excused due to COI), Alfons Kemper, Hank Korth, Alberto Laender, Gerome Miklau, Timos Sellis, Kyu-Young Whang) for technical depth and significance of the research contribution, potential impact on theory and practice, and quality of the presentation. All submissions were of extremely high quality and we congratulate all the nominated students and their departments for their excellent work and contributions to our field. 2009 SIGMOD Undergraduate Awards • Daniel Scott Brotherston (University of Waterloo) -Comparing and visualizing query optimizer search spaces • David M. Lewis (University of Maryland, Baltimore County) -An evaluative comparison of similarity coefficients for binary valued data • Sang-Phil Lim (Sung Kyun Kwan University) -FASTer FTL for enterprise-class flash memory SSDs • Manasi Vartak (WPI) -Recommendation based query relaxation via mapping functions and space partitioning • Shashank Yaduvanshi (IIT Bombay) -An architecture for regulatory compliant database management A complete listing of all SIGMOD Awards is available at: http://www.sigmod. org/awards/ 8 SIGMOD Record, June 2009 (Vol. 38, No. 2) 2009 PODS Best Paper Award Size and Treewidth Bounds for Conjunctive Queries Georg Gottlob, Stephanie Tien Lee (Oxford University), and Gregory J. Valiant (University of California, Berkeley) This paper provides new worst-case bounds for the size and treewith of the result Q(D) of a conjunctive query Q to a database D. We derive bounds for the result size |Q(D)| in terms of structural properties of Q, both in the absence and in the presence of keys and functional dependencies. These bounds are based on a novel "coloring" of the query variables that associates a coloring number C(Q) to each query Q. Using this coloring number, we derive tight bounds for the size of Q(D) in case (i) no functional dependencies or keys are specified, and (ii) simple (one-attribute) keys are given. These results generalize recent size-bounds for join queries obtained by Atserias, Grohe, and Marx (FOCS 2008). An extension of our coloring technique also gives a lower bound for |Q(D)| in the general setting of a query with arbitrary functional dependencies. Our new coloring scheme also allows us to precisely characterize (both in the absence of keys and with simple keys) the treewidth-preserving queries--the queries for which the output treewidth is bounded by a function of the input treewidth. Finally we characterize the queries that preserve the sparsity of the input in the general setting with arbitrary functional dependencies. http://doi.acm. org/10.1145/1559795.1559804 2009 PODS Best Student Paper Award XPath Evaluation in Linear Time with Polynomial Combined Complexity Pawel Parys (Warsaw University) We consider a fragment of XPath 1.0, where attribute and text values may be compared. We show that for any unary query in this fragment, the set of nodes that satisfy the query can be calculated in time linear in the document size and polynomial in the size of the query. The previous algorithm for this fragment a