Reordering Rows for Better Compression: Beyond the Lexicographic Order

Sorting database tables before compressing them improves the compression rate. Can we do better than the lexicographical order? For minimizing the number of runs in a run-length encoding compression scheme, the best approaches to row-ordering are derived from traveling salesman heuristics, although there is a significant trade-off between running time and compression. A new heuristic, Multiple Lists, which is a variant on Nearest Neighbor that trades off compression for a major running-time speedup, is a good option for very large tables. However, for some compression schemes, it is more important to generate long runs rather than few runs. For this case, another novel heuristic, Vortex, is promising. We find that we can improve run-length encoding up to a factor of 3 whereas we can improve prefix coding by up to 80%: these gains are on top of the gains due to lexicographically sorting the table. We prove that the new row reordering is optimal (within 10%) at minimizing the runs of identical values within columns, in a few cases.Comment: to appear in ACM TOD

Distributed top-k aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network

Running deep learning applications on resource constrained devices

The high accuracy of Deep Neural Networks (DNN) come at the expense of high computational cost and memory requirements. During inference, the data is often collected on the edge device which are resource-constrained. The existing solutions for edge deployment include i) executing the entire DNN on the edge (EDGE-ONLY), ii) sending the input from edge to cloud where the DNN is processed (CLOUD-ONLY), and iii) splitting the DNN to execute partially on the edge and partially on the cloud (SPLIT). The choice of deployment between EDGE-ONLY, CLOUD-ONLY and SPLIT is determined by several operating constraints such as device resources and network speed, and application constraints such as latency and accuracy. The EDGE-ONLY approach requires compact DNN with low compute and memory requirements. Thus, the emerging class of DNNs employ low-rank convolutions (LRCONVs) which reduce one or more dimensions compared to the spatial convolutions (CONV). Prior research in hardware accelerators has largely focused on CONVs. The LRCONVs such as depthwise and pointwise convolutions exhibit lower arithmetic intensity and lower data reuse. Thus, LRCONVs result in low hardware utilization and high latency. In our first work, we systematically explore the design space of Cross-layer dataflows to exploit data reuse across layers for emerging DNNs in EDGE-ONLY scenarios. We develop novel fine-grain cross-layer dataflows for LRCONVs that support partial loop dimension completion. Our tool, X-Layer decouples the nested loops in a pipeline and combines them to create a common outer dataflow and several inner dataflows. The CLOUD-ONLY approach can suffer from high latency due to the high transmission cost of large input data from the edge to the cloud. This could be a problem, especially for latency-critical applications. Thankfully, the SPLIT approach reduces latency compared to the CLOUD-ONLY approach. However, existing solutions only split the DNN in floating-point precision. Executing floating-point precision on the edge device can occupy large memory and reduce the potential options for SPLIT solutions. In our second work, we expand and explore the search space of SPLIT solutions by jointly applying mixed-precision post-training quantization and DNN graph split. Our work, Auto-Split finds a balance in the trade-off among the model accuracy, edge device capacity, transmission cost, and the overall latency

Processing long queries against short text: Top-k advertisement matching in news stream applications

© 2017 ACM. Many real applications in real-time news stream advertising call for efficient processing of long queries against short text. In such applications, dynamic news feeds are regarded as queries to match against an advertisement (ad) database for retrieving the k most relevant ads. The existing approaches to keyword retrieval cannot work well in this search scenario when queries are triggered at a very high frequency. To address the problem, we introduce new techniques to significantly improve search performance. First, we devise a two-level partitioning for tight upper bound estimation and a lazy evaluation scheme to delay full evaluation of unpromising candidates, which can bring three to four times performance boosting in a database with 7 million ads. Second, we propose a novel rank-aware block-oriented inverted index to further improve performance. In this index scheme, each entry in an inverted list is assigned a rank according to its importance in the ad. Then, we introduce a block-at-a-time search strategy based on the index scheme to support amuch tighter upper bound estimation and a very early termination. We have conducted experiments with real datasets, and the results show that the rank-aware method can further improve performance by an order of magnitude

Multi-Version Search and Cache-Conscious Ranking Optimization

Organizations and companies archive many versions of digital data such as web pages, internal emails and so on. Such data is critical for internal investigation, regulatory compliance, and electronic discovery. It is estimated that electronic discovery market that leverages archival data will reach $9.9 billions globally in 2017. It is not uncommon for many businesses to retain archived collections for 10 to 15 years. How to archive these versioned data is worth to study and we are facing many challenges including 1) traditional index occupies too much space for versioned data, 2) traditional search is too slow on versioned data, and 3) how to guarantee high accuracy when improving efficiency in new architecture.In this dissertation, we take the opportunity of the fast development of information retrieval and tackle the problem by proposing a new multi-version search architecture with cache-conscious ranking optimization framework. Specifically, we will first discuss our new versioned search architecture. Then, we will talk about a cache-conscious online ranking algorithm to improve the online part. Finally, we will describe a framework to select best blocking methods and parameters for our algorithm to achieve best performance.Firstly, we present our new multi-version search architecture. We propose an approach that uses cluster-based retrieval to quickly narrow the search scope guided by version representatives at Phase 1 and develops a hybrid index structure with adaptive runtime data traversal to speed up Phase 2 search. The hybrid scheme exploits the advantages of forward index and inverted index based on the term characteristics to minimize the time in extracting positional and other feature information during runtime search. We compare several indexing and data traversal options with different time and space tradeoffs and describe evaluation results to demonstrate their effectiveness. The experiment results show that the proposed scheme can be up-to about 4x as fast as the previous work on solid state drives while retaining good relevance.Secondly, we talk about our 2D blocking algorithm to optimize the online ranking part of the system. Multi-tree ensemble models have been proven to be effective for document ranking. Using a large number of trees can improve accuracy, but it takes time to calculate ranking scores of matched documents. We investigate data traversal methods for fast score calculation with a large ensemble and propose a 2D blocking scheme for better cache utilization with simpler code structure compared to previous work. The experiments with several benchmarks show significant acceleration in score calculation without loss of ranking accuracy.Lastly, we describe a framework to fast select best blocking methods and parameters for our 2D blocking algorithm with the help of a full cache analysis. 2D blocking method is very helpful to improve online search efficiency. However, different traversal methods and blocking parameter settings can exhibit different cache and cost behavior depending on data and architectural characteristics. It is very time-consuming to conduct exhaustive search for performance comparison and optimum selection. We provide an analytic comparison of cache blocking methods on their data access performance for an approximation and propose a fast guided sampling scheme to select a traversal method and blocking parameters for effective use of memory hierarchy. The evaluation studies with three datasets show that within a reasonable amount of time, the proposed scheme can identify a highly competitive solution that significantly accelerates score calculation.In summary, we have proposed a new multi-version search architecture with cache-conscious ranking optimization for the online search part and a framework to help fast select best blocking methods and parameters with full cache analysis for the 2D blocking method. By proposing this new versioned search system, we can meet challenges from scalability, efficiency and accuracy in multi-version search, and we believe this work would be useful to future researchers in this direction