12 research outputs found
Finding Top-k Dominance on Incomplete Big Data Using Map-Reduce Framework
Incomplete data is one major kind of multi-dimensional dataset that has random-distributed missing nodes in its dimensions. It is very difficult to retrieve information from this type of dataset when it becomes huge. Finding top-k dominant values in this type of dataset is a challenging procedure. Some algorithms are present to enhance this process but are mostly efficient only when dealing with a small-size incomplete data. One of the algorithms that make the application of TKD query possible is the Bitmap Index Guided (BIG) algorithm. This algorithm strongly improves the performance for incomplete data, but it is not originally capable of finding top-k dominant values in incomplete big data, nor is it designed to do so. Several other algorithms have been proposed to find the TKD query, such as Skyband Based and Upper Bound Based algorithms, but their performance is also questionable. Algorithms developed previously were among the first attempts to apply TKD query on incomplete data; however, all these had weak performances or were not compatible with the incomplete data. This thesis proposes MapReduced Enhanced Bitmap Index Guided Algorithm (MRBIG) for dealing with the aforementioned issues. MRBIG uses the MapReduce framework to enhance the performance of applying top-k dominance queries on huge incomplete datasets. The proposed approach uses the MapReduce parallel computing approach using multiple computing nodes. The framework separates the tasks between several computing nodes that independently and simultaneously work to find the result. This method has achieved up to two times faster processing time in finding the TKD query result in comparison to previously presented algorithms
Optimizing skyline query processing in incomplete data
Given the significance of skyline queries, they are incorporated in various modern applications including personalized recommendation systems as well as decision-making and decision-support systems. Skyline queries are used to identify superior data items in the database. Most of the previously proposed skyline algorithms work on a complete database where the data are always present (non-missing). However, in many contemporary real-world databases, particularly those databases with large cardinality and high dimensionality, such assumption is not necessarily valid. Hence, missing data pose new challenges if the processing skyline queries cannot easily apply those methods that are designed for complete data. This is due to the fact that imperfect data cause the loss of the transitivity property of the skyline method and cyclic dominance. This paper presents a framework called Optimized Incomplete Skyline (OIS) which utilizes a technique that simplifies the skyline process on a database with missing data and helps prune the data items before performing the skyline process. The proposed strategy assures that the number of the domination tests is significantly reduced. A set of experiments has been accomplished using both real and synthetic datasets aimed at validating the performance of the framework. The experiment results confirm that the OIS framework is indeed superior and steadily outperforms the current approaches in terms of the number of domination tests required to retrieve the skylines
Skyline queries computation on crowdsourced- enabled incomplete database
Data incompleteness becomes a frequent phenomenon in a large number of contemporary database applications such as web autonomous databases, big data, and crowd-sourced databases. Processing skyline queries over incomplete databases impose a number of challenges that negatively influence processing the skyline queries. Most importantly, the skylines derived from incomplete databases are also incomplete in which some values are missing. Retrieving skylines with missing values is undesirable, particularly, for
recommendation and decision-making systems. Furthermore, running skyline queries on a database with incomplete data raises a number of issues influence processing skyline queries such as losing the transitivity property of the skyline technique and cyclic dominance between the tuples. The issue of estimating the missing values of skylines has been discussed and examined in the database literature. Most recently, several studies have suggested exploiting the crowd-sourced databases in order to estimate the missing values by generating plausible values using the crowd. Crowd-sourced databases have proved to be a powerful solution to perform user-given tasks by integrating human intelligence and experience to process the tasks. However,
task processing using crowd-sourced incurs additional monetary cost and increases the time latency. Also,
it is not always possible to produce a satisfactory result that meets the user's preferences. This paper proposes an approach for estimating the missing values of the skylines by first exploiting the available data and utilizes the implicit relationships between the attributes in order to impute the missing values of the skylines. This process aims at reducing the number of values to be estimated using the crowd when local estimation is inappropriate. Intensive experiments on both synthetic and real datasets have been accomplished. The experimental results have proven that the proposed approach for estimating the missing values of the skylines over crowd-sourced enabled incomplete databases is scalable and outperforms the other existing approaches
Parallel and progressive approaches for skyline query over probabilistic incomplete database
The advanced productivity of the modern society has created a wide range of similar commodities. However, the descriptions of commodities are always incomplete. Therefore, it is difficult for consumers to make choices. In the face of this problem, skyline query is a useful tool. However, the existing algorithms are unable to address incomplete probabilistic databases. In addition, it is necessary to wait for query completion to obtain even partial results. Furthermore, traditional skyline algorithms are usually serial. Thus, they cannot utilize multi-core processors effectively. Therefore, a parallel progressive skyline query algorithm for incomplete databases is imperative, which provides answers gradually and much faster. To address these problems, we design a new algorithm that uses multi-level grouping, pruning strategies, and pruning tuple transferring, which significantly decreases the computational costs. Experimental results demonstrate that the skyline results can be obtained in a short time. The parallel efficiency for an Octa-core processor reaches 90% on high-dimensional, large databases.<br /
Processing Rank-Aware Queries in Schema-Based P2P Systems
Effiziente Anfragebearbeitung in Datenintegrationssystemen sowie in
P2P-Systemen ist bereits seit einigen Jahren ein Aspekt aktueller
Forschung. Konventionelle Datenintegrationssysteme bestehen aus mehreren
Datenquellen mit ggf. unterschiedlichen Schemata, sind hierarchisch
aufgebaut und besitzen eine zentrale Komponente: den Mediator, der ein
globales Schema verwaltet. Anfragen an das System werden auf diesem
globalen Schema formuliert und vom Mediator bearbeitet, indem relevante
Daten von den Datenquellen transparent für den Benutzer angefragt werden.
Aufbauend auf diesen Systemen entstanden schließlich
Peer-Daten-Management-Systeme (PDMSs) bzw. schemabasierte P2P-Systeme. An
einem PDMS teilnehmende Knoten (Peers) können einerseits als Mediatoren
agieren andererseits jedoch ebenso als Datenquellen. Darüber hinaus sind
diese Peers autonom und können das Netzwerk jederzeit verlassen bzw.
betreten. Die potentiell riesige Datenmenge, die in einem derartigen
Netzwerk verfügbar ist, führt zudem in der Regel zu sehr großen
Anfrageergebnissen, die nur schwer zu bewältigen sind. Daher ist das
Bestimmen einer vollständigen Ergebnismenge in vielen Fällen äußerst
aufwändig oder sogar unmöglich. In diesen Fällen bietet sich die
Anwendung von Top-N- und Skyline-Operatoren, ggf. in Verbindung mit
Approximationstechniken, an, da diese Operatoren lediglich diejenigen
Datensätze als Ergebnis ausgeben, die aufgrund nutzerdefinierter
Ranking-Funktionen am relevantesten für den Benutzer sind. Da durch die
Anwendung dieser Operatoren zumeist nur ein kleiner Teil des Ergebnisses
tatsächlich dem Benutzer ausgegeben wird, muss nicht zwangsläufig die
vollständige Ergebnismenge berechnet werden sondern nur der Teil, der
tatsächlich relevant für das Endergebnis ist.
Die Frage ist nun, wie man derartige Anfragen durch die Ausnutzung dieser
Erkenntnis effizient in PDMSs bearbeiten kann. Die Beantwortung dieser
Frage ist das Hauptanliegen dieser Dissertation. Zur Lösung dieser
Problemstellung stellen wir effiziente Anfragebearbeitungsstrategien in
PDMSs vor, die die charakteristischen Eigenschaften ranking-basierter
Operatoren sowie Approximationstechniken ausnutzen. Peers werden dabei
sowohl auf Schema- als auch auf Datenebene hinsichtlich der Relevanz ihrer
Daten geprüft und dementsprechend in die Anfragebearbeitung einbezogen
oder ausgeschlossen. Durch die Heterogenität der Peers werden Techniken
zum Umschreiben einer Anfrage von einem Schema in ein anderes nötig. Da
existierende Techniken zum Umschreiben von Anfragen zumeist nur konjunktive
Anfragen betrachten, stellen wir eine Erweiterung dieser Techniken vor, die
Anfragen mit ranking-basierten Anfrageoperatoren berücksichtigt. Da PDMSs
dynamische Systeme sind und teilnehmende Peers jederzeit ihre Daten ändern
können, betrachten wir in dieser Dissertation nicht nur wie Routing-Indexe
verwendet werden, um die Relevanz eines Peers auf Datenebene zu bestimmen,
sondern auch wie sie gepflegt werden können. Schließlich stellen wir
SmurfPDMS (SiMUlating enviRonment For Peer Data Management Systems) vor,
ein System, welches im Rahmen dieser Dissertation entwickelt wurde und alle
vorgestellten Techniken implementiert.In recent years, there has been considerable research with respect to query
processing in data integration and P2P systems. Conventional data
integration systems consist of multiple sources with possibly different
schemas, adhere to a hierarchical structure, and have a central component
(mediator) that manages a global schema. Queries are formulated against
this global schema and the mediator processes them by retrieving relevant
data from the sources transparently to the user. Arising from these
systems, eventually Peer Data Management Systems (PDMSs), or schema-based
P2P systems respectively, have attracted attention. Peers participating in
a PDMS can act both as a mediator and as a data source, are autonomous, and
might leave or join the network at will. Due to these reasons peers often
hold incomplete or erroneous data sets and mappings. The possibly huge
amount of data available in such a network often results in large query
result sets that are hard to manage. Due to these reasons, retrieving the
complete result set is in most cases difficult or even impossible. Applying
rank-aware query operators such as top-N and skyline, possibly in
conjunction with approximation techniques, is a remedy to these problems as
these operators select only those result records that are most relevant to
the user. Being aware that in most cases only a small fraction of the
complete result set is actually output to the user, retrieving the complete
set before evaluating such operators is obviously inefficient.
Therefore, the questions we want to answer in this dissertation are how to
compute such queries in PDMSs and how to do that efficiently. We propose
strategies for efficient query processing in PDMSs that exploit the
characteristics of rank-aware queries and optionally apply approximation
techniques. A peer's relevance is determined on two levels: on schema-level
and on data-level. According to its relevance a peer is either considered
for query processing or not. Because of heterogeneity queries need to be
rewritten, enabling cooperation between peers that use different schemas.
As existing query rewriting techniques mostly consider conjunctive queries
only, we present an extension that allows for rewriting queries involving
rank-aware query operators. As PDMSs are dynamic systems and peers might
update their local data, this dissertation addresses not only the problem
of considering such structures within a query processing strategy but also
the problem of keeping them up-to-date. Finally, we provide a system-level
evaluation by presenting SmurfPDMS (SiMUlating enviRonment For Peer Data
Management Systems) -- a system created in the context of this dissertation
implementing all presented techniques