295 research outputs found
ANSWERING WHY-NOT QUESTIONS ON REVERSE SKYLINE QUERIES OVER INCOMPLETE DATA
Recently, the development of the query-based preferences has received considerable attention from researchers and data users. One of the most popular preference-based queries is the skyline query, which will give a subset of superior records that are not dominated by any other records. As the developed version of skyline queries, a reverse skyline query rise. This query aims to get information about the query points that make a data or record as the part of result of their skyline query. Furthermore, data-oriented IT development requires scientists to be able to process data in all conditions. In the real world, there exist incomplete multidimensional data, both because of damage, loss, and privacy. In order to increase the usability over a data set, this study will discuss one of the problems in processing reverse skyline queries over incomplete data, namely the "why-not" problem. The considered solution to this "why-not" problem is advice and steps so that a query point that does not initially consider an incomplete data, as a result, can later make the record or incomplete data as part of the results. In this study, there will be further discussion about the dominance relationship between incomplete data along with the solution of the problem. Moreover, some performance evaluations are conducted to measure the level of efficiency and effectiveness
Exact processing of uncertain top-k queries in multi-criteria settings
Ministry of Education, Singapore under its Academic Research Funding Tier
On Obtaining Stable Rankings
Decision making is challenging when there is more than one criterion to
consider. In such cases, it is common to assign a goodness score to each item
as a weighted sum of its attribute values and rank them accordingly. Clearly,
the ranking obtained depends on the weights used for this summation. Ideally,
one would want the ranked order not to change if the weights are changed
slightly. We call this property {\em stability} of the ranking. A consumer of a
ranked list may trust the ranking more if it has high stability. A producer of
a ranked list prefers to choose weights that result in a stable ranking, both
to earn the trust of potential consumers and because a stable ranking is
intrinsically likely to be more meaningful. In this paper, we develop a
framework that can be used to assess the stability of a provided ranking and to
obtain a stable ranking within an "acceptable" range of weight values (called
"the region of interest"). We address the case where the user cares about the
rank order of the entire set of items, and also the case where the user cares
only about the top- items. Using a geometric interpretation, we propose
algorithms that produce stable rankings. In addition to theoretical analyses,
we conduct extensive experiments on real datasets that validate our proposal
Recommended from our members
Complex Query Operators on Modern Parallel Architectures
Identifying interesting objects from a large data collection is a fundamental problem for multi-criteria decision making applications.In Relational Database Management Systems (RDBMS), the most popular complex query operators used to solve this type of problem are the Top-K selection operator and the Skyline operator.Top-K selection is tasked with retrieving the k-highest ranking tuples from a given relation, as determined by a user-defined aggregation function.Skyline selection retrieves those tuples with attributes offering (pareto) optimal trade-offs in a given relation.Efficient Top-K query processing entails minimizing tuple evaluations by utilizing elaborate processing schemes combined with sophisticated data structures that enable early termination.Skyline query evaluation involves supporting processing strategies which are geared towards early termination and incomparable tuple pruning.The rapid increase in memory capacity and decreasing costs have been the main drivers behind the development of main-memory database systems.Although the act of migrating query processing in-memory has created many opportunities to improve the associated query latency, attaining such improvements has been very challenging due to the growing gap between processor and main memory speeds.Addressing this limitation has been made easier by the rapid proliferation of multi-core and many-core architectures.However, their utilization in real systems has been hindered by the lack of suitable parallel algorithms that focus on algorithmic efficiency.In this thesis, we study in depth the Top-K and Skyline selection operators, in the context of emerging parallel architectures.Our ultimate goal is to provide practical guidelines for developing work-efficient algorithms suitable for parallel main memory processing.We concentrate on multi-core (CPU), many-core (GPU), and processing-in-memory architectures (PIM), developing solutions optimized for high throughout and low latency.The first part of this thesis focuses on Top-K selection, presenting the specific details of early termination algorithms that we developed specifically for parallel architectures and various types of accelerators (i.e. GPU, PIM).The second part of this thesis, concentrates on Skyline selection and the development of a massively parallel load balanced algorithm for PIM architectures.Our work consolidates performance results across different parallel architectures using synthetic and real data on variable query parameters and distributions for both of the aforementioned problems.The experimental results demonstrate several orders of magnitude better throughput and query latency, thus validating the effectiveness of our proposed solutions for the Top-K and Skyline selection operators
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
A systematic literature review of skyline query processing over data stream
Recently, skyline query processing over data stream has gained a lot of attention especially from the database community owing to its own unique challenges. Skyline queries aims at pruning a search space of a potential large multi-dimensional set of objects by keeping only those objects that are not worse than any other. Although an abundance of skyline query processing techniques have been proposed, there is a lack of a Systematic Literature Review (SLR) on current research works pertinent to skyline query processing over data stream. In regard to this, this paper provides a comparative study on the state-of-the-art approaches over the period between 2000 and 2022 with the main aim to help readers understand the key issues which are essential to consider in relation to processing skyline queries over streaming data. Seven digital databases were reviewed in accordance with the Preferred Reporting Items for Systematic Reviews (PRISMA) procedures. After applying both the inclusion and exclusion criteria, 23 primary papers were further examined. The results show that the identified skyline approaches are driven by the need to expedite the skyline query processing mainly due to the fact that data streams are time varying (time sensitive), continuous, real time, volatile, and unrepeatable. Although, these skyline approaches are tailored made for data stream with a common aim, their solutions vary to suit with the various aspects being considered, which include the type of skyline query, type of streaming data, type of sliding window, query processing technique, indexing technique as well as the data stream environment employed. In this paper, a comprehensive taxonomy is developed along with the key aspects of each reported approach, while several open issues and challenges related to the topic being reviewed are highlighted as recommendation for future research direction
Optimization of Dominance Testing in Skyline Queries Using Decision Trees
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Skyline queries identify skyline points, the minimal set of data points that dominate all other data points in a large dataset. The main challenge with skyline queries is executing the skyline query in the shortest possible time. To address and solve skyline query performance issues, we propose a decision tree-based method known as the decision tree-based comparator (DC). This method minimizes unnecessary dominance tests (i.e., pairwise comparisons) by constructing a decision tree based on the dominance testing. DC uses dominance relations that can be obtained from the decision rules of the decision tree to determine incomparability between data points. DC can also be easily applied to improve the performance of various existing skyline query methods. After describing the theoretical background of DC and applying it to existing skyline queries, we present the results of various experiments showing that DC can improve skyline query performance by up to 23.15 times.Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT)Industrial Strategic Technology Development Program funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea)
- …