141 research outputs found
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 fĂĽr den Benutzer angefragt werden.
Aufbauend auf diesen Systemen entstanden schlieĂźlich
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. DarĂĽber hinaus sind
diese Peers autonom und können das Netzwerk jederzeit verlassen bzw.
betreten. Die potentiell riesige Datenmenge, die in einem derartigen
Netzwerk verfĂĽgbar ist, fĂĽhrt zudem in der Regel zu sehr groĂźen
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 fĂĽr 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 geprĂĽft 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 berĂĽcksichtigt. 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
Semantic Query Reformulation in Social PDMS
We consider social peer-to-peer data management systems (PDMS), where each
peer maintains both semantic mappings between its schema and some
acquaintances, and social links with peer friends. In this context,
reformulating a query from a peer's schema into other peer's schemas is a hard
problem, as it may generate as many rewritings as the set of mappings from that
peer to the outside and transitively on, by eventually traversing the entire
network. However, not all the obtained rewritings are relevant to a given
query. In this paper, we address this problem by inspecting semantic mappings
and social links to find only relevant rewritings. We propose a new notion of
'relevance' of a query with respect to a mapping, and, based on this notion, a
new semantic query reformulation approach for social PDMS, which achieves great
accuracy and flexibility. To find rapidly the most interesting mappings, we
combine several techniques: (i) social links are expressed as FOAF (Friend of a
Friend) links to characterize peer's friendship and compact mapping summaries
are used to obtain mapping descriptions; (ii) local semantic views are special
views that contain information about external mappings; and (iii) gossiping
techniques improve the search of relevant mappings. Our experimental
evaluation, based on a prototype on top of PeerSim and a simulated network
demonstrate that our solution yields greater recall, compared to traditional
query translation approaches proposed in the literature.Comment: 29 pages, 8 figures, query rewriting in PDM
Data Sharing in P2P Systems
To appear in Springer's "Handbook of P2P Networking"In this chapter, we survey P2P data sharing systems. All along, we focus on the evolution from simple file-sharing systems, with limited functionalities, to Peer Data Management Systems (PDMS) that support advanced applications with more sophisticated data management techniques. Advanced P2P applications are dealing with semantically rich data (e.g. XML documents, relational tables), using a high-level SQL-like query language. We start our survey with an overview over the existing P2P network architectures, and the associated routing protocols. Then, we discuss data indexing techniques based on their distribution degree and the semantics they can capture from the underlying data. We also discuss schema management techniques which allow integrating heterogeneous data. We conclude by discussing the techniques proposed for processing complex queries (e.g. range and join queries). Complex query facilities are necessary for advanced applications which require a high level of search expressiveness. This last part shows the lack of querying techniques that allow for an approximate query answering
VerdictDB: Universalizing Approximate Query Processing
Despite 25 years of research in academia, approximate query processing (AQP)
has had little industrial adoption. One of the major causes of this slow
adoption is the reluctance of traditional vendors to make radical changes to
their legacy codebases, and the preoccupation of newer vendors (e.g.,
SQL-on-Hadoop products) with implementing standard features. Additionally, the
few AQP engines that are available are each tied to a specific platform and
require users to completely abandon their existing databases---an unrealistic
expectation given the infancy of the AQP technology. Therefore, we argue that a
universal solution is needed: a database-agnostic approximation engine that
will widen the reach of this emerging technology across various platforms.
Our proposal, called VerdictDB, uses a middleware architecture that requires
no changes to the backend database, and thus, can work with all off-the-shelf
engines. Operating at the driver-level, VerdictDB intercepts analytical queries
issued to the database and rewrites them into another query that, if executed
by any standard relational engine, will yield sufficient information for
computing an approximate answer. VerdictDB uses the returned result set to
compute an approximate answer and error estimates, which are then passed on to
the user or application. However, lack of access to the query execution layer
introduces significant challenges in terms of generality, correctness, and
efficiency. This paper shows how VerdictDB overcomes these challenges and
delivers up to 171 speedup (18.45 on average) for a variety of
existing engines, such as Impala, Spark SQL, and Amazon Redshift, while
incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache
License.Comment: Extended technical report of the paper that appeared in Proceedings
of the 2018 International Conference on Management of Data, pp. 1461-1476.
ACM, 201
An Efficient Architecture for Information Retrieval in P2P Context Using Hypergraph
Peer-to-peer (P2P) Data-sharing systems now generate a significant portion of
Internet traffic. P2P systems have emerged as an accepted way to share enormous
volumes of data. Needs for widely distributed information systems supporting
virtual organizations have given rise to a new category of P2P systems called
schema-based. In such systems each peer is a database management system in
itself, ex-posing its own schema. In such a setting, the main objective is the
efficient search across peer databases by processing each incoming query
without overly consuming bandwidth. The usability of these systems depends on
successful techniques to find and retrieve data; however, efficient and
effective routing of content-based queries is an emerging problem in P2P
networks. This work was attended as an attempt to motivate the use of mining
algorithms in the P2P context may improve the significantly the efficiency of
such methods. Our proposed method based respectively on combination of
clustering with hypergraphs. We use ECCLAT to build approximate clustering and
discovering meaningful clusters with slight overlapping. We use an algorithm
MTMINER to extract all minimal transversals of a hypergraph (clusters) for
query routing. The set of clusters improves the robustness in queries routing
mechanism and scalability in P2P Network. We compare the performance of our
method with the baseline one considering the queries routing problem. Our
experimental results prove that our proposed methods generate impressive levels
of performance and scalability with with respect to important criteria such as
response time, precision and recall.Comment: 2o pages, 8 figure
Database Learning: Toward a Database that Becomes Smarter Every Time
In today's databases, previous query answers rarely benefit answering future
queries. For the first time, to the best of our knowledge, we change this
paradigm in an approximate query processing (AQP) context. We make the
following observation: the answer to each query reveals some degree of
knowledge about the answer to another query because their answers stem from the
same underlying distribution that has produced the entire dataset. Exploiting
and refining this knowledge should allow us to answer queries more
analytically, rather than by reading enormous amounts of raw data. Also,
processing more queries should continuously enhance our knowledge of the
underlying distribution, and hence lead to increasingly faster response times
for future queries.
We call this novel idea---learning from past query answers---Database
Learning. We exploit the principle of maximum entropy to produce answers, which
are in expectation guaranteed to be more accurate than existing sample-based
approximations. Empowered by this idea, we build a query engine on top of Spark
SQL, called Verdict. We conduct extensive experiments on real-world query
traces from a large customer of a major database vendor. Our results
demonstrate that Verdict supports 73.7% of these queries, speeding them up by
up to 23.0x for the same accuracy level compared to existing AQP systems.Comment: This manuscript is an extended report of the work published in ACM
SIGMOD conference 201
Probabilistic Message Passing in Peer Data Management Systems
Until recently, most data integration techniques involved central components, e.g., global schemas, to enable transparent access to heterogeneous databases. Today, however, with the democratization of tools facilitating knowledge elicitation in machine-processable formats, one cannot rely on global, centralized schemas anymore as knowledge creation and consumption are getting more and more dynamic and decentralized. Peer Data Management Systems (PDMS) provide an answer to this problem by eliminating the central semantic component and considering instead compositions of local, pair-wise mappings to propagate queries from one database to the others. PDMS approaches proposed so far make the implicit assumption that all mappings used in this way are correct. This obviously cannot be taken as granted in typical PDMS settings where mappings can be created (semi) automatically by independent parties. In this work, we propose a totally decentralized, efficient message passing scheme to automatically detect erroneous mappings in PDMS. Our scheme is based on a probabilistic model where we take advantage of transitive closures of mapping operations to confront local belief on the correctness of a mapping against evidences gathered around the network. We show that our scheme can be efficiently embedded in any PDMS and provide a preliminary evaluation of our techniques on sets of both automatically-generated and real-world schemas
RDF graph summarization: principles, techniques and applications (tutorial)
International audienceThe explosion in the amount of the RDF on the Web has lead to the need to explore, query and understand such data sources. The task is challenging due to the complex and heterogeneous structure of RDF graphs which, unlike relational databases, do not come with a structure-dictating schema. Summarization has been applied to RDF data to facilitate these tasks. Its purpose is to extract concise and meaningful information from RDF knowledge bases, representing their content as faithfully as possible. There is no single concept of RDF summary, and not a single but many approaches to build such summaries; the summarization goal, and the main computational tools employed for summarizing graphs, are the main factors behind this diversity. This tutorial presents a structured analysis and comparison existing works in the area of RDF summarization; it is based upon a recent survey which we co-authored with colleagues [3]. We present the concepts at the core of each approach, outline their main technical aspects and implementation. We conclude by identifying the most pertinent summarization method for different usage scenarios, and discussing areas where future effort is needed
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