Reordering Columns for Smaller Indexes

Column-oriented indexes-such as projection or bitmap indexes-are compressed by run-length encoding to reduce storage and increase speed. Sorting the tables improves compression. On realistic data sets, permuting the columns in the right order before sorting can reduce the number of runs by a factor of two or more. Unfortunately, determining the best column order is NP-hard. For many cases, we prove that the number of runs in table columns is minimized if we sort columns by increasing cardinality. Experimentally, sorting based on Hilbert space-filling curves is poor at minimizing the number of runs.Comment: to appear in Information Science

Sorting improves word-aligned bitmap indexes

Bitmap indexes must be compressed to reduce input/output costs and minimize CPU usage. To accelerate logical operations (AND, OR, XOR) over bitmaps, we use techniques based on run-length encoding (RLE), such as Word-Aligned Hybrid (WAH) compression. These techniques are sensitive to the order of the rows: a simple lexicographical sort can divide the index size by 9 and make indexes several times faster. We investigate row-reordering heuristics. Simply permuting the columns of the table can increase the sorting efficiency by 40%. Secondary contributions include efficient algorithms to construct and aggregate bitmaps. The effect of word length is also reviewed by constructing 16-bit, 32-bit and 64-bit indexes. Using 64-bit CPUs, we find that 64-bit indexes are slightly faster than 32-bit indexes despite being nearly twice as large

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems

EFFICIENT LAYOUTS AND ALGORITHMS FOR MANAGING VERSIONED DATASETS

Version Control Systems were primarily designed to keep track of and provide control over changes to source code and have since provided an excellent way to combat the problem of sharing and editing files in a collaborative setting. The recent surge in data-driven decision making has resulted in a proliferation of datasets elevating them to the level of source code which in turn has led the data analysts to resort to version control systems for the purpose of storing and managing datasets and their versions over time. Unfortunately existing version control systems are poor at handling large datasets primarily due to the underlying assumption that the stored files are relatively small text files with localized changes. Moreover the algorithms used by these systems tend to be fairly simple leading to suboptimal performance when applied to large datasets. In order to address the shortcomings, a key requirement here is to have a Dataset Version Control System (DVCS) that will serve as a common platform to enable data analysts to efficiently store and query dataset versions, track changes to datasets and share datasets between users at ease. Towards this goal, we address the fundamental problem of designing storage layouts for a wide range of datasets to serve as the primary building block for an efficient and scalable DVCS. The key problem in this setting is to compactly store a large number of dataset versions and efficiently retrieve any specific version (or a collection of partial versions). We initiate our study by considering storage-retrieval trade-offs for versions of unstructured dataset such as text files, blobs, etc. where the notion of a partial version is not well-defined. Next, we consider array datasets, i.e., a collection of temporal snapshots (or versions) of multi-dimensional arrays, where the data is predominantly represented in single precision or double precision format. The primary challenge here is to develop efficient compression techniques for the hard-to-compress floating point data due to the high degree of entropy. We observe that the underlying techniques developed for unstructured or array datasets are not well suited for more structured dataset versions -- a version in this setting is defined by a collection of records each of which is uniquely addressable. We carefully explore the design space for building such a system and the various storage-retrieval trade-offs, and discuss how different storage layouts influence those trade-offs. Next, we formulate several problems trading off the version storage and retrieval cost in various ways and design several offline storage layout algorithms that effectively minimize the storage costs while keeping the retrieval costs low. In addition to version retrieval queries, our system also provides support for record provenance queries. Through extensive experiments on large datasets, we demonstrate that our proposed designs can operate at the scale required in most practical scenarios

Content-aware compression for big textual data analysis

A substantial amount of information on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. This thesis proposes techniques for more efficient textual big data analysis suitable for the Hadoop analytic platform. This research explores the direct processing of compressed textual data. The focus is on developing novel compression methods with a number of desirable properties to support text-based big data analysis in distributed environments. The novel contributions of this work include the following. Firstly, a Content-aware Partial Compression (CaPC) scheme is developed. CaPC makes a distinction between informational and functional content in which only the informational content is compressed. Thus, the compressed data is made transparent to existing software libraries which often rely on functional content to work. Secondly, a context-free bit-oriented compression scheme (Approximated Huffman Compression) based on the Huffman algorithm is developed. This uses a hybrid data structure that allows pattern searching in compressed data in linear time. Thirdly, several modern compression schemes have been extended so that the compressed data can be safely split with respect to logical data records in distributed file systems. Furthermore, an innovative two layer compression architecture is used, in which each compression layer is appropriate for the corresponding stage of data processing. Peripheral libraries are developed that seamlessly link the proposed compression schemes to existing analytic platforms and computational frameworks, and also make the use of the compressed data transparent to developers. The compression schemes have been evaluated for a number of standard MapReduce analysis tasks using a collection of real-world datasets. In comparison with existing solutions, they have shown substantial improvement in performance and significant reduction in system resource requirements

Engineering Education and Research Using MATLAB

MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks

Compressing Labels of Dynamic XML Data using Base-9 Scheme and Fibonacci Encoding

The flexibility and self-describing nature of XML has made it the most common mark-up language used for data representation over the Web. XML data is naturally modelled as a tree, where the structural tree information can be encoded into labels via XML labelling scheme in order to permit answers to queries without the need to access original XML files. As the transmission of XML data over the Internet has become vibrant, it has also become necessary to have an XML labelling scheme that supports dynamic XML data. For a large-scale and frequently updated XML document, existing dynamic XML labelling schemes still suffer from high growth rates in terms of their label size, which can result in overflow problems and/or ambiguous data/query retrievals. This thesis considers the compression of XML labels. A novel XML labelling scheme, named “Base-9”, has been developed to generate labels that are as compact as possible and yet provide efficient support for queries to both static and dynamic XML data. A Fibonacci prefix-encoding method has been used for the first time to store Base-9’s XML labels in a compressed format, with the intention of minimising the storage space without degrading XML querying performance. The thesis also investigates the compression of XML labels using various existing prefix-encoding methods. This investigation has resulted in the proposal of a novel prefix-encoding method named “Elias-Fibonacci of order 3”, which has achieved the fastest encoding time of all prefix-encoding methods studied in this thesis, whereas Fibonacci encoding was found to require the minimum storage. Unlike current XML labelling schemes, the new Base-9 labelling scheme ensures the generation of short labels even after large, frequent, skewed insertions. The advantages of such short labels as those generated by the combination of applying the Base-9 scheme and the use of Fibonacci encoding in terms of storing, updating, retrieving and querying XML data are supported by the experimental results reported herein

Hardware Acceleration of the Robust Header Compression (RoHC) Algorithm

With the proliferation of Long Term Evolution (LTE) networks, many cellular carriers are embracing the emerging eld of mobile Voice over Internet Protocol (VoIP). The robust header compression (RoHC) framework was introduced as a part of the LTE Layer 2 stack to compress the large headers of the VoIP packets before transmitted over LTE IP-based architectures. The headers, which are encapsulated Real-time Transport Protocol (RTP)/User Datagram Protocol (UDP)/Internet Protocol (IP) stack, are large compared to the small payload. This header-compression scheme is especially useful for ecient utilization of the radio bandwidth and network resources. In an LTE base-station implementation, RoHC is a processing-intensive algorithm that may be the bottleneck of the system, and thus, may be the limiting factor when it comes to number of users served. In this thesis, a hardware-software and a full-hardware solution are proposed, targeting LTE base-stations to accelerate this computationally intensive algorithm and enhance the throughput and the capacity of the system. The results of both solutions are discussed and compared with respect to design metrics like throughput, capacity, power consumption, chip area and exibility. This comparison is instrumental in taking architectural level trade-o decisions in-order to meet the present day requirements and also be ready to support future evolution. In terms of throughput, a gain of 20% (6250 packets/sec can be processed at a frequency of 150 MHz) is achieved in the HW-SW solution compared to the SW-Only solution by implementing the Cyclic Redundancy Check (CRC) and the Least Signicant Bit(LSB) encoding blocks as hardware accelerators . Whereas, a Full-HW implementation leads to a throughput of 45 times (244000 packets/sec can be processed at a frequency of 100 MHz) the throughput of the SW-Only solution. However, the full-HW solution consumes more Lookup Tables (LUTs) when it is synthesized on an Field-Programmable Gate Array (FPGA) platform compared to the HW-SW solution. In Arria II GX, the HW-SW and the full-HW solutions use 2578 and 7477 LUTs and consume 1.5 and 0.9 Watts, respectively. Finally, both solutions are synthesized and veried on Altera's Arria II GX FPGA