Top PDF Compression of Very Sparse Column Oriented Data

Compression of Very Sparse Column Oriented Data

Compression of Very Sparse Column Oriented Data

paper is that some lightweight methods could be useful for very sparse datasets as well. Before going any further, we observe the existence of null suppression techniques useful for encoding very sparse datasets composed by a significant amount of null fields. For instance, Abadi et al. (2007) propose innovative page layouts to be used depending on the sparsity of data, as Figure 2 shows. If data is very sparse, null values are not represented at all. Instead, the pages keep a list of the positions where data is not null and another list with the values contained in those positions. This page layout was proposed as part of the C-Store database (the academic version of the Vertica database). The pitfall of the approach is that it was designed to work with fixed length columns in order to enable trivial vectorization. On the other hand, in this paper we consider variable length columns that could not be smoothly accommodated in a vector.
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Efficien and Scalable Data Evolution with Column Oriented Databases

Efficien and Scalable Data Evolution with Column Oriented Databases

Column Oriented Databases. Notable database man- agement systems that store data via columns include Mon- etDB [12], LucidDB [1] and C-Store [33]. They all support relational databases and data warehouses, on which SQL queries can be executed. C-Store additionally supports hy- brid structures of both row oriented and column oriented storages, as well as overlapping columns to speed up query processing. A detailed investigation of compression schemes on column oriented databases and guidance for choosing these schemes are presented in [5]. [19] compares the per- formance of column oriented databases and row oriented databases with respect to various factors, such as the num- ber of columns accessed by a query, L2 cache prefetching, selectivity, tuple width, etc., and demonstrates that colum- n oriented databases are in general more efficient than row oriented databases for answering queries that do not access many attributes. [3] studies how column stores handle wide tables and sparse data. An optimization strategy that joins columns into rows as late as possible when answering queries is presented in [7]. [4] shows that column oriented databas- es are well suitable for handling vertically partitioned RDF data, which achieves an order of magnitude improvemen- t in efficiency compared with row oriented databases. [6] conducts a comprehensive study of the fundamental differ- ences between row stores and column stores during query processing, investigates whether the performance advantage of column stores can be achieved by row stores using vertical partitioning, as well as how much each optimization strate- gy affects the performance of column stores. All these works on column stores study the query processing efficiency, while data evolution on column stores has not been addressed.
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Fast Retrieval with Column Store using RLE Compression Algorithm

Fast Retrieval with Column Store using RLE Compression Algorithm

ColumnOriented database has drawn a lot of attention in last few years. The source of column-oriented database systems can be seen beginning from 1970s, but it was not until 2000s that some researches and applications started to be done. In the past recent years some column store databases namely MonetDB [1] [2] and C-Store [3] has been introduced by their authors, with the claim that their performance gains are quite noticeable against traditional approaches. The column oriented database specifically designed for analytic purpose overcome the flaws encountered in traditional DBMS by storing, managing, querying, data based on column instead of row. Column-Stores approach, store each column separately rather than storing entire row i.e. instead of retrieving a record or row at a time, an entire column is retrieved, only necessary columns in a query are accessed rather entire rows, I/O activities as well as overall query response time is reduced and access becomes faster because much more relevant Column can be accessed in a shorter period of time [4]. Moreover there are some opportunities for storage size optimizations available in column-store because data in a column oriented database can be better compressed than those in a row-oriented database, values in a column are much more homogenous than in a row. The compression of a column- oriented database may reduce its size up to 20 times, this thing providing a higher performance and reduced storage costs [5][6].
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DATA MIGRATION METHODOLOGY FROM SQL TO COLUMN ORIENTED DATABASES (HBase)

DATA MIGRATION METHODOLOGY FROM SQL TO COLUMN ORIENTED DATABASES (HBase)

Column Families – Data in a row are grouped together as Column Families. Each Column Family has one more Columns and these Columns in a family are stored together in a low level storage file known as HFile. Column Families form the basic unit of physical storage to which certain HBase features like compression are applied. Hence it‘s important that proper care be taken when designing Column Families in table. The table above shows Customer and Sales Column Families. The Customer Column Family is made up 2 columns – Name and City, whereas the Sales Column Families is made up to 2 columns – Product and Amount.
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A Framework for Building Hypercubes Using MapReduce

A Framework for Building Hypercubes Using MapReduce

In this paper we present and analyze a generic framework that allows the hypercube generation to be easily done within a MapReduce infrastructure, providing all the advantages of the new Big Data analysis paradigm but without dealing with any specific interface to the lower level distributed system implementation (Hadoop). Furthermore, we show how executing the framework for different data storage model configurations (i.e. row or column oriented) and compression techniques can considerably improve the response time of this type of workload for the currently available simulated data of the mission.
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An Efficient Fingerprint Compression Algorithm Using Sparse Coding

An Efficient Fingerprint Compression Algorithm Using Sparse Coding

A novel algorithm for adapting dictionaries so as to represent signals sparsely are described below. Given a set of training signals ,the dictionary that leads to the best possible representations for each member in this set with strict sparsity constraints. Here uses the K-SVD algorithm that addresses the above task, generalizing the k-means algorithm. The K-SVD is an iterative method that alternates between sparse coding of the examples based on the current dictionary and an update process for the dictionary atoms so as to better fit the data. The update of the dictionary columns is done jointly with an update of the sparse representation coefficients related to it, resulting in accelerated convergence. The K-SVD algorithm is flexible and can work with any pursuit method, thereby tailoring the dictionary to the application in mind.
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Does Relevance Matter to Data Mining Research?

Does Relevance Matter to Data Mining Research?

Data mining (DM) and knowledge discovery are intelligent tools that help to accumulate and process data and make use of it. We review several existing frameworks for DM research that originate from different paradigms. These DM frameworks mainly address various DM algorithms for the different steps of the DM process. Recent research has shown that many real-world problems require integration of several DM algorithms from different paradigms in order to produce a better solution elevating the importance of practice-oriented aspects also in DM research. In this paper we strongly emphasize that DM research should also take into account the relevance of research, not only the rigor of it. Under relevance of research in general, we understand how good this research is in terms of the utility of its results. This chapter motivates development of such a new framework for DM research that would explicitly include the concept of relevance. We introduce the basic idea behind such framework and propose one sketch for the new framework for DM research based on results achieved in the information systems area having some tradition related to the relevance aspects of research.
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GraphZIP: a clique-based sparse graph compression method

GraphZIP: a clique-based sparse graph compression method

Massive graphs are ubiquitous and at the heart of many real-world problems and applications ranging from the World Wide Web to social networks. As a result, tech- niques for compressing graphs have become increasingly important and remains a challenging and unsolved problem. In this work, we propose a graph compression and encoding framework called GraphZIP based on the observation that real-world graphs often form many cliques of a large size. Using this as a foundation, the proposed tech- nique decomposes a graph into a set of large cliques, which is then used to compress and represent the graph succinctly. In particular, disk-resident and in-memory graph encodings are proposed and shown to be effective with three important benefits. First, it reduces the space needed to store the graph on disk (or other permanent storage device) and in-memory. Second, GraphZIP reduces IO traffic involved in using the graph. Third, it reduces the amount of work involved in running an algorithm on the graph. The experiments demonstrate the scalability, flexibility, and effectiveness of the clique-based compression techniques using a collection of networks from various domains.
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Image similarity using sparse representation and compression distance

Image similarity using sparse representation and compression distance

A new line of research uses compression methods to measure the similarity between signals. Two signals are considered similar if one can be compressed significantly when the information of the other is known. The existing compression-based similarity methods, although successful in the discrete one dimensional domain, do not work well in the context of images. This paper proposes a sparse representation-based approach to encode the information content of an image using information from the other image, and uses the compactness (sparsity) of the representation as a measure of its compressibility (how much can the image be compressed) with respect to the other image. The more sparse the representation of an image, the better it can be compressed and the more it is similar to the other image. The efficacy of the proposed measure is demonstrated through the high accuracies achieved in image clustering, retrieval and classification.
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Automated Statistical Modeling for Data Mining David Stephenson 1

Automated Statistical Modeling for Data Mining David Stephenson 1

Our efforts focus on the family of generalized linear models (GLZs), which generalize the family of general linear models (GLMs), which, in turn, generalize linear models. Roughly speaking, the main idea behind GLZs is that there is a random response variable Y and a smooth, differentiable link function E such that E(Y) regresses to a polynomial function, g, of the predictor variables. We will focus our attention on cases where g is a multi-level hierarchical function, where summands involve an arbitrary product of predictor variables (i.e., terms contain products of predictor variables with exponents of zero or one). The term hierarchical refers to the requirement that a model containing a higher-level interaction term must also include all corresponding lower-level interactions. For example, the existence of the 3- level interaction term XYZ in a model requires the existence of the terms X, Y, Z, XY, XZ, and YZ. The family of generalized linear models encompasses a great many of the data sets in which we at Wagner Associates were interested.
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In-Memory Databases

In-Memory Databases

Almost the synonym for processing of big unstructured data is Hadoop. Companies like Facebook, Yahoo!, Twitter use this system to store and process their data. Hadoop is based on MapReduce framework created by Google in 2004 and its main purpose was to store and process a huge amount of unstructured data. However, the latest versions of Hadoop are also improving in processing of structured data, this system is not really a database system, rather the file system. Data are multiple stored in HDFS (Hadoop File Systems), which create clusters and even collections of clusters to enable the most important characteristic of Hadoop – massive parallelism. To access data, first step, the map function maps all the data by chosen key through the data clusters. The second step is the reduce function to reduce all duplicate values found by map function (13).
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Prestressed Stainless Steel Stayed Columns with Two Crossarms

Prestressed Stainless Steel Stayed Columns with Two Crossarms

The ANSYS model involved BEAM188 elements for the central and crossarm tubes and LINK180 elements for the cable stays, with the same boundary conditions as mentioned in the Chapter 2.1 (connections between column and crossarms are assumed to be rigid, between stays and column/crossarms are ideal hinges). The meshing study resulted into division of L/250 and a/25 as satisfactory one. First, the required initial deflections were introduced, followed by the relevant prestressing of stays through their thermal change (i.e. by cooling). Finally, axial deflections to the central column (giving an external load) up to collapse were imposed. A standard Newton-Raphson iteration was used. To verify the analytical values, first GNIA was used, with elastic material behavior, followed by GMNIA for stainless steel material.
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Stabilized Sparse Online Learning for Sparse Data

Stabilized Sparse Online Learning for Sparse Data

function and when the sparsity level of the input data is low, such as the case of the Arcene data set. These results also indicate that the truncate gradient algorithm fails to extract significant features and to obtain sparse solutions, which motivates the development of techniques discussed in this paper. On the other hand, the variances in the percentage of nonzero features of the proposed algorithm are reduced by approximately a magnitude of 10. The contribution of stabilization is demonstrated again in terms of feature selection. Although RDA achieves very low sparsity in Dorothea, such a behavior is not observed in other data sets. It is shown to result in particularly denser weight vector with highly sparse data, such as RCV1, indicating its weakness in identifying truly informative features when information is scarce. FOBOS demonstrates overall poorer performance in terms of inducing sparse weight vector as compared to RDA and the proposed algorithm. Similar to its performance in terms of test error, the proposed algorithm delivers highly stable results in feature selection regardless of the choice of loss function and of random permutations of the data. We also achieve sufficient sparse result owing to stability selection which prevent noisy features from adding back to the stable set of variables in the online setting. On the other hand, the high sparsity in weight vector does not overshadow the generalizability performance as the informative truncation with unbiased shrinkage underlies a better estimation of the selection probability for the construction of the set of stable variables.
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Sparse representation based hyperspectral image compression and classification

Sparse representation based hyperspectral image compression and classification

Wavelets provide a signal representation in which some of the coefficients represent long data lags corresponding to a narrow band, low frequency range, and some of the coefficients represent short data lags corresponding to a wide band, high frequency range [Shapiro 1993]. Wavelet theory studies filter design methods such that the filter bank is perfectly reconstructing, and both the low-pass and high-pass filters have finite impulse responses [Vetterli and Kovacevic 1995]. DWT is often used to find a compact multiresolution representation of signals, including images. One-dimensional DWT decorrelates a signal by splitting the data into two half-rate subse- quences, i.e., low- and high-frequency half-bands, carrying information on the approximation and detail of the original signal, respectively. The two-channel decomposition can be repeated re- spectively on the low-pass and high-pass subband samples of a previous filtering stage to provide a multi-resolution decomposition of the input signal. However, in most DWT decompositions, only the low-pass output is further decomposed, and we refer to this type as dyadic decomposi- tion. Figure 4.4 shows a 1D, three-level dyadic wavelet decomposition by means of a filter bank scheme with low-pass and high-pass filters denoted as g[n] and h[n], respectively. At each level of the decomposition, the low-pass filter preserves the low frequencies of a signal while elimi- nating the high frequencies, thus resulting in a coarse approximation of the original signal. The high-pass filter, conversely, preserves the high frequencies of the signal such as edges, texture and detail, while removing the low frequencies. In summary, the high-pass samples in the tree- structured transform are wavelet transform coefficients, and the low-pass samples are of vanishing importance when the number of decomposition levels becomes large. However, practical DWT implementations must include the low resolution subband samples in the reconstruction.
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ON ANALYZING THE DATABASE PERFORMANCE FOR DIFFERENT CLASSES OF XML DOCUMENTS BASED ON THE USED STORAGE APPROACH

ON ANALYZING THE DATABASE PERFORMANCE FOR DIFFERENT CLASSES OF XML DOCUMENTS BASED ON THE USED STORAGE APPROACH

In recent years the amount and usage of XML data grew rapidly and today it is used for many purposes such as data transfer, configuration files or to store information. So, the safe, efficient and reliable stor- age for such documents becomes more and more im- portant. Until a few years ago there existed - be- sides classical file systems - only two options for stor- ing XML documents: either native XML databases (e.g. Apache Xindice or Tamino (Sch¨oning, 2003)) or XML enabled relational databases providing an XML data type. In the second case XML documents are either stored as a character large object (clobbing) or shredded into relational database tables (object re- lational mapping). Recently large database vendors such as Oracle or IBM developed another alternative, the so called hybrid database systems. They store both, relational data and XML data natively by pro- viding two separate storage systems. However, all alternatives have certain advantages and drawbacks. Choosing the appropriate technique depends on the application and is a non trivial task. Even though there exist a number of XML benchmarks they are usually focused on a specific application domain (e.g., financial data) and so far have not considered the ad-
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Data Compression Algorithms and Comparison between Arithmetic and Huffman Encoding

Data Compression Algorithms and Comparison between Arithmetic and Huffman Encoding

This type of compression works by reducing how much waste space is in a piece of data. For example, if you receive a data package which contains "AAAAABBBB", you could compress that into "5A4B", which has the same meaning but takes up less space. This type of compression is called "run-length encoding", because you define how long the "run" of a character is. In the above example, there are two runs: a run of 5 A's, and another of 4 B's.

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Compressive Deformation Simulation of Regularly Cell Structured Materials with Various Column Connectivity

Compressive Deformation Simulation of Regularly Cell Structured Materials with Various Column Connectivity

configuration on the plateau stress and strain is thought to be much important in addition to the relative density and matrix alloy yielding stress. In the present theoretical model, both materials and geometric non-linearitries are directly consid- ered to describe the elasto–plastic collapsing, yielding and bending deformation before final densification process. The calculated series of deformed configuration in cells demon- strates the effect of initial cellular geometry on the deformation mode. Furthermore, the calculated relationship between macro–stress and macro–strain is compared with the measured mechanical response in quasi–static compression test.
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A Survey of Accuracy Evaluation Metrics of Recommendation Tasks

A Survey of Accuracy Evaluation Metrics of Recommendation Tasks

As described above, the goal of the offline evaluation is to filter algorithms so that only the most promising need undergo expensive online tests. Thus, the data used for the offline evaluation should match as closely as possible the data the designer expects the recommender system to face when deployed online. Care must be exercised to ensure that there is no bias in the distributions of users, items and ratings selected. For example, in cases where data from an existing system (perhaps a system without a recommender) is available, the experimenter may be tempted to pre-filter the data by excluding items or users with low counts, in order to reduce the costs of experimentation. In doing so, the experimenter should be mindful that this involves a trade-off, since this introduces a systematic bias in the data. If necessary, randomly sampling users and items may be a preferable method for reducing data, although this can also introduce other biases into the experiment (e.g., this could tend to favor algorithms that work better with more sparse data).
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Application of Bayes Compressed Sensing in Image rocessing

Application of Bayes Compressed Sensing in Image rocessing

ABSTRACT: As the demand for information increases, signal bandwidth carrying messages becomes increasingly wider, requirements on acquisition rate and processing rate become increasingly higher and the difficulty in broadband signal processing increases, existing analog-digital converters, transmission bandwidths, software and hardware systems and data storage devices cannot satisfy the needs, acquisition, storage, transmission and processing of signal bring huge pressure. Based on this, a nonparametric hierarchical Bayes learning method of image compression sparse representation was proposed, and a nonparametric hierarchical Bayes mixed factor model under Dirichle process distribution was established specific to the sparsity of geometrical model data of Bayes learning of lower-dimensional signal model, the consistency of subspaces, manifold and analysis of mixed factors, etc. The model can study the low-order Gaussian Mixture Model of high-dimensional image data restricted in low-dimensional subspaces, obtain the number of mixed factors and factors through automatic learning of given data set and use it as prior knowledge of data for the reestablishment of image compressed sensing. Effectiveness of the model was analyzed through simulation experiment.
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Homomorphic Elgamal Encryption in NoSQL for Secure Cloud

Homomorphic Elgamal Encryption in NoSQL for Secure Cloud

ABSTRACT: NoSQL adopted by number of leading organizations. RDBMS doesn’t fit with today’s Big Data scenario. Rapid growths of data not only ask for storage but ask for its security too. This paper provides the specific method to secure data on cloud with the help of Homophormic Elgamal Encryption (HEE). To implement our work, column oriented database (HBase), an awesome storage explanation for NoSQL database is chosen. Data encrypted using proposed HEE scheme, using Microsoft Azure toolkit and basic .NET package with SQL server. Thus, only authentic user can access the NoSQL database by following this proposed method. Cause of the proposed functionality, HEE algorithm allows users to send encrypted data, which would compute the cipher text without decrypting it and send back same encrypted effect to a server or user.
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