31 research outputs found
A Bandit Approach to Maximum Inner Product Search
There has been substantial research on sub-linear time approximate algorithms
for Maximum Inner Product Search (MIPS). To achieve fast query time,
state-of-the-art techniques require significant preprocessing, which can be a
burden when the number of subsequent queries is not sufficiently large to
amortize the cost. Furthermore, existing methods do not have the ability to
directly control the suboptimality of their approximate results with
theoretical guarantees. In this paper, we propose the first approximate
algorithm for MIPS that does not require any preprocessing, and allows users to
control and bound the suboptimality of the results. We cast MIPS as a Best Arm
Identification problem, and introduce a new bandit setting that can fully
exploit the special structure of MIPS. Our approach outperforms
state-of-the-art methods on both synthetic and real-world datasets.Comment: AAAI 201
Revisiting Projection-Free Optimization for Strongly Convex Constraint Sets
We revisit the Frank-Wolfe (FW) optimization under strongly convex constraint
sets. We provide a faster convergence rate for FW without line search, showing
that a previously overlooked variant of FW is indeed faster than the standard
variant. With line search, we show that FW can converge to the global optimum,
even for smooth functions that are not convex, but are quasi-convex and
locally-Lipschitz. We also show that, for the general case of (smooth)
non-convex functions, FW with line search converges with high probability to a
stationary point at a rate of , as long as the
constraint set is strongly convex -- one of the fastest convergence rates in
non-convex optimization.Comment: Extended version of paper accepted at AAAI-19, 19 pages, 10 figure
BlinkDB: queries with bounded errors and bounded response times on very large data
In this paper, we present BlinkDB, a massively parallel, approximate query engine for running interactive SQL queries on large volumes of data. BlinkDB allows users to trade-off query accuracy for response time, enabling interactive queries over massive data by running queries on data samples and presenting results annotated with meaningful error bars. To achieve this, BlinkDB uses two key ideas: (1) an adaptive optimization framework that builds and maintains a set of multi-dimensional stratified samples from original data over time, and (2) a dynamic sample selection strategy that selects an appropriately sized sample based on a query's accuracy or response time requirements. We evaluate BlinkDB against the well-known TPC-H benchmarks and a real-world analytic workload derived from Conviva Inc., a company that manages video distribution over the Internet. Our experiments on a 100 node cluster show that BlinkDB can answer queries on up to 17 TBs of data in less than 2 seconds (over 200 x faster than Hive), within an error of 2-10%.National Science Foundation (U.S.) (CISE Expeditions Award CCF-1139158)United States. Defense Advanced Research Projects Agency (XData Award FA8750-12-2-0331)
Blink and it's done: Interactive queries on very large data
In this demonstration, we present BlinkDB, a massively parallel, sampling-based approximate query processing framework for running interactive queries on large volumes of data. The key observation in BlinkDB is that one can make reasonable decisions in the absence of perfect answers. BlinkDB extends the Hive/HDFS stack and can handle the same set of SPJA (selection, projection, join and aggregate) queries as supported by these systems. BlinkDB provides real-time answers along with statistical error guarantees, and can scale to petabytes of data and thousands of machines in a fault-tolerant manner. Our experiments using the TPC-H benchmark and on an anonymized real-world video content distribution workload from Conviva Inc. show that BlinkDB can execute a wide range of queries up to 150x faster than Hive on MapReduce and 10--150x faster than Shark (Hive on Spark) over tens of terabytes of data stored across 100 machines, all with an error of 2--10%.National Science Foundation (U.S.) (CISE Expeditions Award CCF-1139158)QUALCOMM Inc.Amazon.com (Firm)Google (Firm)SAP CorporationBlue GojiCisco Systems, Inc.Cloudera, Inc.Ericsson, Inc.General Electric CompanyHewlett-Packard CompanyIntel CorporationMarkLogic CorporationMicrosoft CorporationNetAppOracle CorporationSplunk Inc.VMware, Inc.United States. Defense Advanced Research Projects Agency (Contract FA8650-11-C-7136
Knowing when you're wrong: Building fast and reliable approximate query processing systems
Modern data analytics applications typically process massive amounts of data on clusters of tens, hundreds, or thousands of machines to support near-real-time decisions.The quantity of data and limitations of disk and memory bandwidth often make it infeasible to deliver answers at interactive speeds. However, it has been widely observed that many applications can tolerate some degree of inaccuracy. This is especially true for exploratory queries on data, where users are satisfied with "close-enough" answers if they can come quickly. A popular technique for speeding up queries at the cost of accuracy is to execute each query on a sample of data, rather than the whole dataset. To ensure that the returned result is not too inaccurate, past work on approximate query processing has used statistical techniques to estimate "error bars" on returned results. However, existing work in the sampling-based approximate query processing (S-AQP) community has not validated whether these techniques actually generate accurate error bars for real query workloads. In fact, we find that error bar estimation often fails on real world production workloads. Fortunately, it is possible to quickly and accurately diagnose the failure of error estimation for a query. In this paper, we show that it is possible to implement a query approximation pipeline that produces approximate answers and reliable error bars at interactive speeds.National Science Foundation (U.S.) (CISE Expeditions Award CCF-1139158)Lawrence Berkeley National Laboratory (Award 7076018)United States. Defense Advanced Research Projects Agency (XData Award FA8750-12-2-0331)Amazon.com (Firm)Google (Firm)SAP CorporationThomas and Stacey Siebel FoundationApple Computer, Inc.Cisco Systems, Inc.Cloudera, Inc.EMC CorporationEricsson, Inc.Facebook (Firm
A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching
Entity Matching (EM) is a core data cleaning task, aiming to identify
different mentions of the same real-world entity. Active learning is one way to
address the challenge of scarce labeled data in practice, by dynamically
collecting the necessary examples to be labeled by an Oracle and refining the
learned model (classifier) upon them. In this paper, we build a unified active
learning benchmark framework for EM that allows users to easily combine
different learning algorithms with applicable example selection algorithms. The
goal of the framework is to enable concrete guidelines for practitioners as to
what active learning combinations will work well for EM. Towards this, we
perform comprehensive experiments on publicly available EM datasets from
product and publication domains to evaluate active learning methods, using a
variety of metrics including EM quality, #labels and example selection
latencies. Our most surprising result finds that active learning with fewer
labels can learn a classifier of comparable quality as supervised learning. In
fact, for several of the datasets, we show that there is an active learning
combination that beats the state-of-the-art supervised learning result. Our
framework also includes novel optimizations that improve the quality of the
learned model by roughly 9% in terms of F1-score and reduce example selection
latencies by up to 10x without affecting the quality of the model.Comment: accepted for publication in ACM-SIGMOD 2020, 15 page