Self-organizing tuple reconstruction in column-stores

Column-stores gained popularity as a promising physical design alternative. Each attribute of a relation is physically stored as a separate column allowing queries to load only the required attributes. The overhead incurred is on-the-fly tuple reconstruction for multi-attribute queries. Each tuple reconstruction is a join of two columns based on tuple IDs, making it a significant cost component. The ultimate physical design is to have multiple presorted copies of each base table such that tuples are already appropriately organized in multiple different orders across the various columns. This requires the ability to predict the workload, idle time to prepare, and infrequent updates. In this paper, we propose a novel design, \emph{partial sideways cracking}, that minimizes the tuple rec

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

A hybrid data structure for dense keys in in-memory database systems

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 71-72).This thesis presents a data structure which performs well for in-memory indexing of keys that are unevenly distributed into clusters with a high density of keys. This pattern is prevalent, for example, in systems that use tables with keys where one field is auto-incremented. These types of tables are widely used. The proposed data structure consists of a B+ Tree with intervals as keys, and arrays as values. Each array holds a cluster of values, while the clusters themselves are managed by the B+ Tree for space and cache efficiency. Using the H-Tree as an in-memory indexing structure for an implementation of the TPC-C benchmark sped up the transaction processing time by up to 50% compared to an implementation based on B+Trees, and showed even more dramatic performance gains in the presence of few and large clusters of data.by José Alberto Muñiz Navarro.M.Eng

EClass: An execution classification approach to improving the energy-efficiency of software via machine learning

Energy efficiency at the software level has gained much attention in the past decade. This paper presents a performance-aware frequency assignment algorithm for reducing processor energy consumption using Dynamic Voltage and Frequency Scaling (DVFS). Existing energy-saving techniques often rely on simplified predictions or domain knowledge to extract energy savings for specialized software (such as multimedia or mobile applications) or hardware (such as NPU or sensor nodes). We present an innovative framework, known as EClass, for general-purpose DVFS processors by recognizing short and repetitive utilization patterns efficiently using machine learning. Our algorithm is lightweight and can save up to 52.9% of the energy consumption compared with the classical PAST algorithm. It achieves an average savings of 9.1% when compared with an existing online learning algorithm that also utilizes the statistics from the current execution only. We have simulated the algorithms on a cycle-accurate power simulator. Experimental results show that EClass can effectively save energy for real life applications that exhibit mixed CPU utilization patterns during executions. Our research challenges an assumption among previous work in the research community that a simple and efficient heuristic should be used to adjust the processor frequency online. Our empirical result shows that the use of an advanced algorithm such as machine learning can not only compensate for the energy needed to run such an algorithm, but also outperforms prior techniques based on the above assumption. © 2011 Elsevier Inc. All rights reserved.postprin

Flexible and efficient IR using array databases

textabstractThe Matrix Framework is a recent proposal by IR researchers to flexibly represent all important information retrieval models in a single multi-dimensional array framework. Computational support for exactly this framework is provided by the array database system SRAM (Sparse Relational Array Mapping) that works on top of a DBMS. Information retrieval models can be specified in its comprehension-based array query language, in a way that directly corresponds to the underlying mathematical formulas. SRAM efficiently stores sparse arrays in (compressed) relational tables and translates and optimizes array queries into relational queries. In this work, we describe a number of array query optimization rules and demonstrate their effect on text retrieval in the TREC TeraByte track (TREC-TB) efficiency task, using the Okapi BM25 model as our example. It turns out that these optimization rules enable SRAM to automatically translate the BM25 array queries into the relational equivalent of inverted list processing including compression, score materialization and quantization, such as employed by custom-built IR systems. The use of the high-performance MonetDB/X100 relational backend, that provides transparent database compression, allows the system to achieve very fast response times with good precision and low resource usage

SHAPES : Easy and high-level memory layouts

CPU speeds have vastly exceeded those of RAM. As such, developers who aim to achieve high performance on modern architectures will most likely need to consider how to use CPU caches effectively, hence they will need to consider how to place data in memory so as to exploit spatial locality and achieve high memory bandwidth. Performing such manual memory optimisations usually sacrifices readability, maintainability, memory safety, and object abstraction. This is further exacerbated in managed languages, such as Java and C#, where the runtime abstracts away the memory from the developer and such optimisations are, therefore, almost impossible. To that extent, we present in this thesis a language extension called SHAPES . SHAPES aims to offer developers more fine-grained control over the placement of data, without sacrificing memory safety or object abstraction, hence retaining the expressiveness and familiarity of OOP. SHAPES introduces the concepts of pools and layouts; programmers group related objects into pools, and specify how objects are laid out in these pools. Classes and types are annotated by pool parameters, which allow placement aspects to be changed orthogonally to how the business logic operates on the objects in the pool. These design decisions disentangle business logic and memory concerns. We provide a formal model of SHAPES , present its type and memory safety model, and its translation into a low-level language. We present our reasoning as to why we can expect SHAPES to be compiled in an efficient manner in terms of the runtime representation of objects and the access to their fields. Moreover, we present SHAPES -z, an implementation of SHAPES as an embeddable language, and shapeszc , the compiler for SHAPES -z. We provide our our design and implementation considerations for SHAPES -z and shapeszc . Finally, we evaluate the performance of SHAPES and SHAPES -z through case studies.Open Acces