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2 edition of Ad-hoc top-k query answering for data streams. found in the catalog.

Ad-hoc top-k query answering for data streams.

Nikolaos Sarkas

Ad-hoc top-k query answering for data streams.

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  • 16 Currently reading

Published .
Written in English


About the Edition

The efficient evaluation of top-k queries has been an active research topic and many different instantiations of the problem, in a variety of settings, have been studied. However, current techniques are not directly applicable to highly dynamic environments and on-line applications, like data streams.Recently, techniques supporting top-k queries on data streams have been introduced. Such techniques are restrictive however, as they can only efficiently report top-k answers with respect to a pre-specified (as opposed to ad-hoc) set of queries. In this thesis we introduce a novel geometric representation for the top-k query problem that allows us to raise this restriction. Utilizing notions of geometric arrangements, we design and analyze algorithms for incrementally maintaining and querying a data set organized in an arrangement representation under streaming updates. The performance of our core technique is augmented by incorporating tuple pruning strategies. A thorough experimental study evaluates the efficiency of the proposed technique.

The Physical Object
Pagination48 leaves.
Number of Pages48
ID Numbers
Open LibraryOL21218717M
ISBN 109780494272954


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Ad-hoc top-k query answering for data streams. by Nikolaos Sarkas Download PDF EPUB FB2

Ad-hoc Top-k Query Answering for Data Streams Gautam Das University of Texas at Arlington [email protected] Dimitrios Gunopulos University of California, Riverside [email protected] Nick Koudas University of Toronto [email protected] Nikos Sarkas University of Toronto [email protected] ABSTRACT A top-k query retrieves the k highest scoring.

Request PDF | Ad-hoc Top-k Query Answering for Data Streams. | A top-k query retrieves the k highest scoring tuples from a data set with respect to a scoring function defined on the attribut es of. @MISC{Das07ad-hoctop-k, author = {Gautam Das and Nick Koudas and et al.}, title = { Ad-hoc Top-k Query Answering for Data Streams}, year = {}} Share.

OpenURL. Abstract. A top-k query retrieves the k highest scoring tuples from a data set with respect to a scoring function defined on the attributes of a tuple. The efficient evaluation of. INDEXING for top-k query answering in the dual plane Each tuple t = (x1, x2) is mapped to a line et: y = (1 − x2)x + (1 − x1) in the dual plane.

A query Q can be represented as a point p(Q) = (w2 / w1, 0), where w1, w2are the weights of its scoring function. Theorem 2. ABSTRACT Ad-hoc Top-k Query Answering for Data Streams. By Gautam Das and Nick Koudas. Abstract. A top-k query retrieves the k highest scoring tuples from a data set with respect to a scoring function defined on the attributes of a tuple.

The efficient evaluation of top-k queries has been an active research topic and many different Author: Gautam Das and Nick Koudas.

Top-k queries over data streams is a well studied problem. There exists numerous systems allowing to process contin-uous queries over sliding windows.

At the opposite, non-append only streams call for ad-hoc solutions, e.g. tailor-made solutions implemented in a mainstream programming language. In the meantime, the Stream API and lambda. Answering ad hoc aggregate queries from data streams using PAT window, t is (trivially) a prefix aggregate cell.

If t is not unique, then there exists a prefix aggregate cell which has the same value as t on every dimension. In other words, the set of prefix aggregate cells covers all tuples in.

Cho M., Pei J., and Wang K., Answering ad hoc aggregate queries from data streams using prefix aggregate trees, Knowledge and Information Systems 12(3) (Aug ), [4] Hahn C.J., Warren S.G., and Eastman R., Extended edited synoptic cloud reports from ships and land stations over the globe, This book examines the problem of relevant query answering over the Web and provides a comprehensive overview of relevant query answering over streaming and distributed data.

In recent years, Web applications that combine highly dynamic data streams with data distributed over the Web to provide relevant answers have attracted increasing attention. Query processing in the data stream model of computation comes with its own unique challenges.

Unbounded Memory Requirements: Since data streams are potentially unbounded in size, the amount of storage required to compute an exact answer to a data stream query may also grow without bound.

A traditional database management system typically processes a stream of ad-hoc queries over relatively static data. In contrast, a DSMS evaluates static (long-running) queries on streaming data, making a single pass over the data and using limited working memory.

Frequent elements and top-k queries constitute an important class of queries for data stream analysis n applications require answers for both frequent elements and top-k queries on the same addition, the ever increasing data rates call for providing fast answers to the queries, and researchers have been looking towards exploiting specialized hardware for this purpose.

Top-k queries over the streams of interest allow limiting results to relevant content, while continuous processing of such queries is the most effective approach in large scale systems.

Current systems fail in combining continuous top- k processing with. Section snippets Related work. Top-k query processing is a widely studied research aim is, based on a set of objects and a specific scoring function, to retrieve those objects with the highest (lowest) ive studies on the problem can be found in [11], [20], [21].

In [1], a class of queries that report the top-k (highest) values observed in distributed data streams is. In the same manner, an ad hoc query does not reside in the computer or the database manager but is dynamically created depending on the needs of the data user. In the past, for users to analyze various kinds of data, multiple sets of queries are being constructed.

Mostly static data, ad-hoc one-time queries Fire the queries at the data, return result sets “Store and query” Focus: concurrent reads & writes, efficient use of I/O, maximize transaction throughput, transactional consistency, historical analysis Data Stream Systems Mostly transient data, continuous queries.

These sources of data are called Data Streams. Computers play a much more active role in the current trends in decision support and data analysis. Data mining algorithms search for hypothesis, evaluate and suggest patterns. The pattern discovery process requires online ad-hoc queries, not previously defined, that are successively refined.

Elements and Top-k Queries over Data Streams Sudipto Das Divyakant Agrawal Amr El Abbadi Department of Computer Science University of California, Santa Barbara Santa Barbara, CAUSA {sudipto, agrawal, amr}@ ABSTRACT Frequent elements and top-kqueries constitute an im-portant class of queries for data stream analysis appli-cations.

Streamlining Financial Analytics: Answering Ad Hoc Queries For all the diversity and natural wonder our world presents, it’s primarily powered by financials at the end of the day. For the institutions whose successes hinge on the state of financial markets, it’s essential to stay abreast of all day-to-day changes.

Nikos Sarkas's 11 research works with citations and reads, including: Dense Subgraph Maintenance under Streaming Edge Weight Updates for Real-time Story Identification.

Realtime Streaming Analytics (static queries given once that do not change, they process data as they come in without storing. CEP, Apache Strom, Apache Samza etc., are examples of this.

Realtime Interactive/Ad-hoc Analytics (user issue ad-hoc dynamic queries and system responds). Druid, SAP Hana, VoltDB, MemSQL, Apache Drill are examples of this. Gautam Das, Dimitrios Gunopulos, Nick Koudas, Nikos Sarkas: Ad-hoc Top-k Query Answering for Data Streams.

VLDB Sharmila Subramaniam, Themis Palpanas, Dimitris Papadopoulos, Vana Kalogeraki, Dimitrios Gunopulos: Online Outlier Detection in Sensor Data Using Non-Parametric Models. VLDB Existing Approaches for Query of Patient Data.

Strategies for ad hoc querying of data from a CPRS/CSDMS fall within two extremes. At one extreme, queries are issued directly against the source data, or a copy with the same structure residing on a CPU different from the transactional system.

views to answer ad hoc top-k queries. We then address the prob-lem of identifying the most promising (in terms of performance) views to use for query answering in the presence of a collection of views.

We formalize both problems and present efficient algo-rithms for their solution. We also discuss several extensions of the basic problems in. The Text REtrieval Conference (TREC) is an ongoing series of workshops focusing on a list of different information retrieval (IR) research areas, or tracks.

It is co-sponsored by the National Institute of Standards and Technology (NIST) and the Intelligence Advanced Research Projects Activity (part of the office of the Director of National Intelligence), and began in as part of the. In this section, we formally define the problem of a top-k query over incomplete data stream (Topk-iDS), which takes into account missing attribute values during the top-k query processing.

Incomplete data stream Incomplete data stream. We first define the data models for incomplete data stream and the sliding window over a data stream. Ad-hoc top-k query answering for data streams. In: VLDB, (). C.: Introduction to Algorithms. MIT/McGraw-Hill, (). Depth estimation for ranking query optimization.

Distributed top-k. Recent data stream systems such as TelegraphCQ have employed the well-known property of duality between data and queries. In these systems, query processing methods are classified into two dual categories -- data-initiative and query-initiative-- depending on whether query processing is initiated by selecting a data element or a gh the duality property has been widely recognized.

Gautam Das, Dimitrios Gunopulos, Nick Koudas, Nikos Sarkas: Ad-hoc Top-k Query Answering for Data Streams. VLDB (Acceptance rate 17%) Arjun Dasgupta, Gautam Das, Heikki Mannila: A random walk approach to sampling hidden databases.

[Date Streams 2] Title = Distributed Top-k Monitoring. Book Title = Proceedings of the ACM SIGMOD international conference on Management of data. Author = Brian Babcock and Chris Olston. pages =year = [Date Streams 3] Title = Adaptive Filters for Continuous Queries over Distributed Data Streams.

Book Title = Proceedings of. Ad-hoc Top-k Query Answering for Data Streams Gautam Das (University of Texas at Arling-ton, USA), Dimitrios Gunopulos (University of California – Riverside, USA), Nick Koudas, Nikos Sarkas (University of Toronto, Canada) Research Session 5: Text Databases.

An Efficient Algorithm for top-k Queries on Uncertain Data Streams Abstract: We tackle the problem of answering maximum probabilistic top-k tuple set queries.

We use a sliding-window model on uncertain data streams and present an efficient algorithm for processing sliding-window queries on uncertain streams. Gautam Das, Dimitrios Gunopulos, Nick Koudas, Nikos Sarkas: Ad-hoc Top-k Query Answering for Data Streams.

VLDB Benjamin Arai, Gautam Das, Dimitrios Gunopulos, Nick Koudas: Anytime Measures for Top-k Algorithms. VLDB   SQL Server uses several new techniques, such as caching ad-hoc queries and automatic parameterization. However, the queries that SQL Server automatically parameterizes are limited.

Use the following methods to make sure that the query plans are parameterized and can be reused more effectively. Q3. Is the following statement True or False. You can cater for every possible adhoc query when processing data streams.

True B. False Q4. Is the following statement True or False. When mining data streams, we prepare for adhoc queries that are most common to our application. True B. False Q5. Answering Top-k queries Using Views, VLDB Nov 23 Tuesday.

Anitha Royappan Gautam Das, Dimitrios Gunopulos, Nick Koudas, Nikos Sarkas. Ad-hoc Top-k Query Answering for Data Streams, VLDB Optional Readings: Data Streams: tutorial 1 and tutorial 2. Nov 25 Thursday. where an ad hoc database includes redundant data — may produce a starter query expressing the union of the two datasets, having observed that they share the same schema and verlap significantly.

This exploration of the influence on the starter queries from the data — as opposed to query logs, the schema, or user input — is the key. Top-k query monitoring over the data stream. Starting from the mid s, various works addressed the problem of top-k approximate join of data stream [32,34,40] by introducing novel techniques for incremental query evaluation.

to horizontally join data streams from multiple devices holding the same type of data. More importantly, frequently the device may have to vertically join data streams from different devices announcing their presence. Existingsolutionsfor traditionalmobilesystems are inapplicable[18, 28].

This is due to the dynamicnatureof ad hoc wireless networks. The annual international conference on Database and Expert Systems Applications (DEXA) is now well established as a reference scientific event.

The reader will find in this volume a collection of scientific papers that represent the state of the art of - search in the domain of data, information and knowledge management, intelligent systems, and their applications. A really good question to ask at this point is: Why am I covering ad-hoc query tuning before other major topics such as loading data?

The answer is simple: If end-users cannot quickly view reports on the data, then the data warehouse is a bust. In fact, you can judge your data warehouse's success by simply asking the following: Do the end-users.Jilin Han, Le Gruenwald and Tyrrel Conway, "Data Mining in Gene Expression Data Analysis: A Survey a book chapter in "Processing and Managing Complex Data for Decision Support edited by Jerome Darmont and O.

Boussaid, Idea Group, ISBN:March Query Answering Ontology Mappings query results Figure The basic set-up for Ontology-Based Data Access formulate queries using the terms de ned by the ontology, using some formal query language.

In other words, queries are formulated according to the end-users’ view of .