A fast retrieval method for local or distributed data

In this paper, we propose an improvement to an approach to data retrieval which is performed in only one access to a bucket hash table or file. The idea behind it, is to let the system assign one digit to the record key so that the hashed new record key is "forced " to fall in a bucket according to some practical criteria. From a user point of view this forced hash procedure could be thought of as a “user-system cooperating code assignment”, since the user is free to code an object to be retrieved but the system may append s a digit to that code. For one access retrieval purposes, the new code key-digit is used to find its address. However, should the digit is not known, the retrieval process will find the key in its surrounding, provided it exists. In this approach it is unnecessary a bucket overflow area of any kind, since this method allows a high load factor for practical use. In the event of the hash table is nearly full, a simple procedure could be ran to extend the table size either by keeping the original digit or assigning new ones. For distributed data sets this methodology shows an appealing performance in real life and simulation results.Eje: Programación concurrenteRed de Universidades con Carreras en Informática (RedUNCI

Sliding Block Hashing (Slick) -- Basic Algorithmic Ideas

We present {\bf Sli}ding Blo{\bf ck} Hashing (Slick), a simple hash table data structure that combines high performance with very good space efficiency. This preliminary report outlines avenues for analysis and implementation that we intend to pursue

A fast retrieval method for local or distributed data

In this paper, we propose an improvement to an approach to data retrieval which is performed in only one access to a bucket hash table or file. The idea behind it, is to let the system assign one digit to the record key so that the hashed new record key is "forced " to fall in a bucket according to some practical criteria. From a user point of view this forced hash procedure could be thought of as a “user-system cooperating code assignment”, since the user is free to code an object to be retrieved but the system may append s a digit to that code. For one access retrieval purposes, the new code key-digit is used to find its address. However, should the digit is not known, the retrieval process will find the key in its surrounding, provided it exists. In this approach it is unnecessary a bucket overflow area of any kind, since this method allows a high load factor for practical use. In the event of the hash table is nearly full, a simple procedure could be ran to extend the table size either by keeping the original digit or assigning new ones. For distributed data sets this methodology shows an appealing performance in real life and simulation results.Eje: Programación concurrenteRed de Universidades con Carreras en Informática (RedUNCI

Random hypergraphs for hashing-based data structures

This thesis concerns dictionaries and related data structures that rely on providing several random possibilities for storing each key. Imagine information on a set S of m = |S| keys should be stored in n memory locations, indexed by [n] = {1,…,n}. Each object x [ELEMENT OF] S is assigned a small set e(x) [SUBSET OF OR EQUAL TO] [n] of locations by a random hash function, independent of other objects. Information on x must then be stored in the locations from e(x) only. It is possible that too many objects compete for the same locations, in particular if the load c = m/n is high. Successfully storing all information may then be impossible. For most distributions of e(x), however, success or failure can be predicted very reliably, since the success probability is close to 1 for loads c less than a certain load threshold c^* and close to 0 for loads greater than this load threshold. We mainly consider two types of data structures: • A cuckoo hash table is a dictionary data structure where each key x [ELEMENT OF] S is stored together with an associated value f(x) in one of the memory locations with an index from e(x). The distribution of e(x) is controlled by the hashing scheme. We analyse three known hashing schemes, and determine their exact load thresholds. The schemes are unaligned blocks, double hashing and a scheme for dynamically growing key sets. • A retrieval data structure also stores a value f(x) for each x [ELEMENT OF] S. This time, the values stored in the memory locations from e(x) must satisfy a linear equation that characterises the value f(x). The resulting data structure is extremely compact, but unusual. It cannot answer questions of the form “is y [ELEMENT OF] S?”. Given a key y it returns a value z. If y [ELEMENT OF] S, then z = f(y) is guaranteed, otherwise z may be an arbitrary value. We consider two new hashing schemes, where the elements of e(x) are contained in one or two contiguous blocks. This yields good access times on a word RAM and high cache efficiency. An important question is whether these types of data structures can be constructed in linear time. The success probability of a natural linear time greedy algorithm exhibits, once again, threshold behaviour with respect to the load c. We identify a hashing scheme that leads to a particularly high threshold value in this regard. In the mathematical model, the memory locations [n] correspond to vertices, and the sets e(x) for x [ELEMENT OF] S correspond to hyperedges. Three properties of the resulting hypergraphs turn out to be important: peelability, solvability and orientability. Therefore, large parts of this thesis examine how hyperedge distribution and load affects the probabilities with which these properties hold and derive corresponding thresholds. Translated back into the world of data structures, we achieve low access times, high memory efficiency and low construction times. We complement and support the theoretical results by experiments.Diese Arbeit behandelt Wörterbücher und verwandte Datenstrukturen, die darauf aufbauen, mehrere zufällige Möglichkeiten zur Speicherung jedes Schlüssels vorzusehen. Man stelle sich vor, Information über eine Menge S von m = |S| Schlüsseln soll in n Speicherplätzen abgelegt werden, die durch [n] = {1,…,n} indiziert sind. Jeder Schlüssel x [ELEMENT OF] S bekommt eine kleine Menge e(x) [SUBSET OF OR EQUAL TO] [n] von Speicherplätzen durch eine zufällige Hashfunktion unabhängig von anderen Schlüsseln zugewiesen. Die Information über x darf nun ausschließlich in den Plätzen aus e(x) untergebracht werden. Es kann hierbei passieren, dass zu viele Schlüssel um dieselben Speicherplätze konkurrieren, insbesondere bei hoher Auslastung c = m/n. Eine erfolgreiche Speicherung der Gesamtinformation ist dann eventuell unmöglich. Für die meisten Verteilungen von e(x) lässt sich Erfolg oder Misserfolg allerdings sehr zuverlässig vorhersagen, da für Auslastung c unterhalb eines gewissen Auslastungsschwellwertes c* die Erfolgswahrscheinlichkeit nahezu 1 ist und für c jenseits dieses Auslastungsschwellwertes nahezu 0 ist. Hauptsächlich werden wir zwei Arten von Datenstrukturen betrachten: • Eine Kuckucks-Hashtabelle ist eine Wörterbuchdatenstruktur, bei der jeder Schlüssel x [ELEMENT OF] S zusammen mit einem assoziierten Wert f(x) in einem der Speicherplätze mit Index aus e(x) gespeichert wird. Die Verteilung von e(x) wird hierbei vom Hashing-Schema festgelegt. Wir analysieren drei bekannte Hashing-Schemata und bestimmen erstmals deren exakte Auslastungsschwellwerte im obigen Sinne. Die Schemata sind unausgerichtete Blöcke, Doppel-Hashing sowie ein Schema für dynamisch wachsenden Schlüsselmengen. • Auch eine Retrieval-Datenstruktur speichert einen Wert f(x) für alle x [ELEMENT OF] S. Diesmal sollen die Werte in den Speicherplätzen aus e(x) eine lineare Gleichung erfüllen, die den Wert f(x) charakterisiert. Die entstehende Datenstruktur ist extrem platzsparend, aber ungewöhnlich: Sie ist ungeeignet um Fragen der Form „ist y [ELEMENT OF] S?“ zu beantworten. Bei Anfrage eines Schlüssels y wird ein Ergebnis z zurückgegeben. Falls y [ELEMENT OF] S ist, so ist z = f(y) garantiert, andernfalls darf z ein beliebiger Wert sein. Wir betrachten zwei neue Hashing-Schemata, bei denen die Elemente von e(x) in einem oder in zwei zusammenhängenden Blöcken liegen. So werden gute Zugriffszeiten auf Word-RAMs und eine hohe Cache-Effizienz erzielt. Eine wichtige Frage ist, ob Datenstrukturen obiger Art in Linearzeit konstruiert werden können. Die Erfolgswahrscheinlichkeit eines naheliegenden Greedy-Algorithmus weist abermals ein Schwellwertverhalten in Bezug auf die Auslastung c auf. Wir identifizieren ein Hashing-Schema, das diesbezüglich einen besonders hohen Schwellwert mit sich bringt. In der mathematischen Modellierung werden die Speicherpositionen [n] als Knoten und die Mengen e(x) für x [ELEMENT OF] S als Hyperkanten aufgefasst. Drei Eigenschaften der entstehenden Hypergraphen stellen sich dann als zentral heraus: Schälbarkeit, Lösbarkeit und Orientierbarkeit. Weite Teile dieser Arbeit beschäftigen sich daher mit den Wahrscheinlichkeiten für das Vorliegen dieser Eigenschaften abhängig von Hashing Schema und Auslastung, sowie mit entsprechenden Schwellwerten. Eine Rückübersetzung der Ergebnisse liefert dann Datenstrukturen mit geringen Anfragezeiten, hoher Speichereffizienz und geringen Konstruktionszeiten. Die theoretischen Überlegungen werden dabei durch experimentelle Ergebnisse ergänzt und gestützt

Fast Data Analytics by Learning

Today, we collect a large amount of data, and the volume of the data we collect is projected to grow faster than the growth of the computational power. This rapid growth of data inevitably increases query latencies, and horizontal scaling alone is not sufficient for real-time data analytics of big data. Approximate query processing (AQP) speeds up data analytics at the cost of small quality losses in query answers. AQP produces query answers based on synopses of the original data. The sizes of the synopses are smaller than the original data; thus, AQP requires less computational efforts for producing query answers, thus can produce answers more quickly. In AQP, there is a general tradeoff between query latencies and the quality of query answers; obtaining higher-quality answers requires longer query latencies. In this dissertation, we show we can speed up the approximate query processing without reducing the quality of the query answers by optimizing the synopses using two approaches. The two approaches we employ for optimizing the synopses are as follows: 1. Exploiting past computations: We exploit the answers to the past queries. This approach relies on the fact that, if two aggregation involve common or correlated values, the aggregated results must also be correlated. We formally capture this idea using a probabilistic distribution function, which is then used to refine the answers to new queries. 2. Building task-aware synopses: By optimizing synopses for a few common types of data analytics, we can produce higher quality answers (or more quickly for certain target quality) to those data analytics tasks. We use this approach for constructing synopses optimized for searching and visualizations. For exploiting past computations and building task-aware synopses, our work incorporates statistical inference and optimization techniques. The contributions in this dissertation resulted in up to 20x speedups for real-world data analytics workloads.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138598/1/pyongjoo_1.pd

Scalable Storage for Digital Libraries

I propose a storage system optimised for digital libraries. Its key features are its heterogeneous scalability; its integration and exploitation of rich semantic metadata associated with digital objects; its use of a name space; and its aggressive performance optimisation in the digital library domain

Heuristics and multi-dimensional physical database design

An expert system approach has recently been used in parameter selection for VSAM (Virtual Storage Access Method) file organisation [AL87a]. This system has been developed to aid in-house users to apply relevant facts and heuristics to optimise VSAM file design. Multi-dimensional physical database design is more sophisticated and complicated than VSAM file design. The expert system approach can be applied to select and tune physical database design for various applications. A great deal of work has been done in developing diverse algorithms or access methods to organise automated information on secondary storage devices [FA86b] [FR86] [FR88] [GU84] [HU88a] [KS88a] [KS86] [L087] [NI84] [OR88b] [OR86] [OT85] [R081], etc. However, little work has been done to enable designers to select an access method which matches a projected application profile (features and requirements) and perceived strengths and weaknesses of candidate algorithms. This thesis considers a number of grid based algorithms and makes expert assessments of each according to its strengths and weaknesses. It analyses features of various access methods and using expert knowledge matches features for a range of m-d (multi­ dimensional) algorithms with corresponding characteristics of an application. The knowledge-based system presented in this thesis can be applied either manually or computerised to give a systematic approach to m-d algorithm selection. A system is proposed to (1) heuristically select an initial algorithm; (2) describe how the selection process is evaluated against actual m-d algorithm performance and (3) show how the results of the evaluation can be used to refine expert knowledge embodied in the selection system. Heuristic assessments are given for several m-d access algorithms. Examples are presented to show how these heuristics are used to select a m-d access algorithm for a specific application. It is reasonable to suppose that the initial heuristic assessments are not entirely accurate. A tuning mechanism for the system heuristics is given in section 4.9. The system selection process is thereby, able to adjust to real world results. Finally, we present a simple example to illustrate how the proposed system works

Spell checkers and correctors : a unified treatment

The aim of this dissertation is to provide a unified treatment of various spell checkers and correctors. Firstly, the spell checking and correcting problems are formally described in mathematics in order to provide a better understanding of these tasks. An approach that is similar to the way in which denotational semantics used to describe programming languages is adopted. Secondly, the various attributes of existing spell checking and correcting techniques are discussed. Extensive studies on selected spell checking/correcting algorithms and packages are then performed. Lastly, an empirical investigation of various spell checking/correcting packages is presented. It provides a comparison and suggests a classification of these packages in terms of their functionalities, implementation strategies, and performance. The investigation was conducted on packages for spell checking and correcting in English as well as in Northern Sotho and Chinese. The classification provides a unified presentation of the strengths and weaknesses of the techniques studied in the research. The findings provide a better understanding of these techniques in order to assist in improving some existing spell checking/correcting applications and future spell checking/correcting package designs and implementations.Dissertation (MSc)--University of Pretoria, 2009.Computer Scienceunrestricte

RoleSim* : scaling axiomatic role-based similarity ranking on large graphs

RoleSim and SimRank are among the popular graph-theoretic similarity measures with many applications in, e.g., web search, collaborative filtering, and sociometry. While RoleSim addresses the automorphic (role) equivalence of pairwise similarity which SimRank lacks, it ignores the neighboring similarity information out of the automorphically equivalent set. Consequently, two pairs of nodes, which are not automorphically equivalent by nature, cannot be well distinguished by RoleSim if the averages of their neighboring similarities over the automorphically equivalent set are the same. To alleviate this problem: 1) We propose a novel similarity model, namely RoleSim*, which accurately evaluates pairwise role similarities in a more comprehensive manner. RoleSim* not only guarantees the automorphic equivalence that SimRank lacks, but also takes into account the neighboring similarity information outside the automorphically equivalent sets that are overlooked by RoleSim. 2) We prove the existence and uniqueness of the RoleSim* solution, and show its three axiomatic properties (i.e., symmetry, boundedness, and non-increasing monotonicity). 3) We provide a concise bound for iteratively computing RoleSim* formula, and estimate the number of iterations required to attain a desired accuracy. 4) We induce a distance metric based on RoleSim* similarity, and show that the RoleSim* metric fulfills the triangular inequality, which implies the sum-transitivity of its similarity scores. 5) We present a threshold-based RoleSim* model that reduces the computational time further with provable accuracy guarantee. 6) We propose a single-source RoleSim* model, which scales well for sizable graphs. 7) We also devise methods to scale RoleSim* based search by incorporating its triangular inequality property with partitioning techniques. Our experimental results on real datasets demonstrate that RoleSim* achieves higher accuracy than its competitors while scaling well on sizable graphs with billions of edges