10 research outputs found
Efficient Computation of Subspace Skyline over Categorical Domains
Platforms such as AirBnB, Zillow, Yelp, and related sites have transformed
the way we search for accommodation, restaurants, etc. The underlying datasets
in such applications have numerous attributes that are mostly Boolean or
Categorical. Discovering the skyline of such datasets over a subset of
attributes would identify entries that stand out while enabling numerous
applications. There are only a few algorithms designed to compute the skyline
over categorical attributes, yet are applicable only when the number of
attributes is small.
In this paper, we place the problem of skyline discovery over categorical
attributes into perspective and design efficient algorithms for two cases. (i)
In the absence of indices, we propose two algorithms, ST-S and ST-P, that
exploits the categorical characteristics of the datasets, organizing tuples in
a tree data structure, supporting efficient dominance tests over the candidate
set. (ii) We then consider the existence of widely used precomputed sorted
lists. After discussing several approaches, and studying their limitations, we
propose TA-SKY, a novel threshold style algorithm that utilizes sorted lists.
Moreover, we further optimize TA-SKY and explore its progressive nature, making
it suitable for applications with strict interactive requirements. In addition
to the extensive theoretical analysis of the proposed algorithms, we conduct a
comprehensive experimental evaluation of the combination of real (including the
entire AirBnB data collection) and synthetic datasets to study the practicality
of the proposed algorithms. The results showcase the superior performance of
our techniques, outperforming applicable approaches by orders of magnitude
Contributions à l’Optimisation de Requêtes Multidimensionnelles
Analyser les données consiste à choisir un sous-ensemble des dimensions qui les décriventafin d'en extraire des informations utiles. Or, il est rare que l'on connaisse a priori les dimensions"intéressantes". L'analyse se transforme alors en une activité exploratoire où chaque passe traduit par une requête. Ainsi, il devient primordiale de proposer des solutions d'optimisationde requêtes qui ont une vision globale du processus plutôt que de chercher à optimiser chaque requêteindépendamment les unes des autres. Nous présentons nos contributions dans le cadre de cette approcheexploratoire en nous focalisant sur trois types de requêtes: (i) le calcul de bordures,(ii) les requêtes dites OLAP (On Line Analytical Processing) dans les cubes de données et (iii) les requêtesde préférence type skyline
Efficient optimization for L-extSKY recommendations
based on Regular Grid) za dobivanje L-extSKY objekata u jednom jedinom podprostoru. Međutim, u okruženju s više korisnika, sustav obično simultano rješava mnogostruke podprostorne L-extSKY preporuke. U ovom radu stoga predstavljamo učinkoviti algoritam AOMSR (Algorithm for Optimizing Multiple Subspace L-extSKY Recommendations) u svrhu značajnog smanjenja ukupnog vremena odziva. Nadalje, raspravljamo o dvije interesantne varijacije L-extSKY preporuke, tj. globalnom ograničenju L-extSKY preporuke i lokalnom ograničenju L-extSKY preporuke, koje su od praktičnog značaja i pokazuju kako se naš algoritam može primijeniti u svrhu njihove učinkovite obrade. Detaljna teoretska analiza i velik broj eksperimenata kojima se demonstrira naše rješenje su i efikasni i efektivni.L-extSKY recommendation has recently received a lot of attention in information retrieval community. Literature [1] proposes an algorithm EARG (Effi-cient Approach based on Regular Grid) to produce the L-extSKY objects in one single subspace. However, in multi-user environments, the system gener-ally handles multiple subspace L-extSKY recommendations simultaneously. Hence, in this paper, we present an efficient algorithm AOMSR (Algorithm for Optimizing Multiple Subspace L-extSKY Recommendations) to remarkably reduce the total response time. Furthermore, we discuss two interesting variations of L-extSKY recommendation, i.e., global constraint L-extSKY recommendation and local constraint L-extSKY recommendation, which are meaningful in practice, and show how our algorithm can be applied for their efficient processing. Detailed theoretical analyses and extensive experiments that demonstrate our solution are both efficient and effective
Effective Space Usage Estimation for Sliding-Window Skybands
Skyline query computes all the “best” elements which are
not dominated by any other elements and thus is very important
for decision-making applications. Recently, it is generalized
to skyband query and a k-skyband query returns
those elements dominated by no more than k, of other elements.
To incorporate the skyband operator into the stream engine
for monitoring skybands over sliding windows, space usage
estimation for skyband operator becomes a critical issue in
the query optimizer. In this paper, we firstly introduce the
skyband sketch as the cost model. Based on the cost model,
we propose an approach for estimating the space usage of
skyband operator over sliding windows of data streams under
the assumptions of statistical independence across dimensions,
no duplicate values over each dimension, and dimension
domains totally ordered. Experiments verify that
our approaches can estimate the space usage effectively over
arbitrarily distributed data. To the best of our knowledge,
this is the first work that attempts to address the issue and
proposes effective approaches to solve it
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