1,631 research outputs found
Subset Queries in Relational Databases
In this paper, we motivated the need for relational database systems to
support subset query processing. We defined new operators in relational
algebra, and new constructs in SQL for expressing subset queries. We also
illustrated the applicability of subset queries through different examples
expressed using extended SQL statements and relational algebra expressions. Our
aim is to show the utility of subset queries for next generation applications.Comment: 15 page
Goal Directed Relative Skyline Queries in Time Dependent Road Networks
The Wireless GIS technology is progressing rapidly in the area of mobile
communications. Location-based spatial queries are becoming an integral part of
many new mobile applications. The Skyline queries are latest apps under
Location-based services. In this paper we introduce Goal Directed Relative
Skyline queries on Time dependent (GD-RST) road networks. The algorithm uses
travel time as a metric in finding the data object by considering multiple
query points (multi-source skyline) relative to user location and in the user
direction of travelling. We design an efficient algorithm based on Filter
phase, Heap phase and Refine Skyline phases. At the end, we propose a dynamic
skyline caching (DSC) mechanism which helps to reduce the computation cost for
future skyline queries. The experimental evaluation reflects the performance of
GD-RST algorithm over the traditional branch and bound algorithm for skyline
queries in real road networks
Preference Queries
The handling of user preferences is becoming an increasingly important issue
in present-day information systems. Among others, preferences are used for
information filtering and extraction to reduce the volume of data presented to
the user. They are also used to keep track of user profiles and formulate
policies to improve and automate decision making.
We propose here a simple, logical framework for formulating preferences as
preference formulas. The framework does not impose any restrictions on the
preference relations and allows arbitrary operation and predicate signatures in
preference formulas. It also makes the composition of preference relations
straightforward. We propose a simple, natural embedding of preference formulas
into relational algebra (and SQL) through a single winnow operator
parameterized by a preference formula. The embedding makes possible the
formulation of complex preference queries, e.g., involving aggregation, by
piggybacking on existing SQL constructs. It also leads in a natural way to the
definition of further, preference-related concepts like ranking. Finally, we
present general algebraic laws governing the winnow operator and its
interaction with other relational algebra operators. The preconditions on the
applicability of the laws are captured by logical formulas. The laws provide a
formal foundation for the algebraic optimization of preference queries. We
demonstrate the usefulness of our approach through numerous examples.Comment: 34 page
Semantic Optimization Techniques for Preference Queries
Preference queries are relational algebra or SQL queries that contain
occurrences of the winnow operator ("find the most preferred tuples in a given
relation"). Such queries are parameterized by specific preference relations.
Semantic optimization techniques make use of integrity constraints holding in
the database. In the context of semantic optimization of preference queries, we
identify two fundamental properties: containment of preference relations
relative to integrity constraints and satisfaction of order axioms relative to
integrity constraints. We show numerous applications of those notions to
preference query evaluation and optimization. As integrity constraints, we
consider constraint-generating dependencies, a class generalizing functional
dependencies. We demonstrate that the problems of containment and satisfaction
of order axioms can be captured as specific instances of constraint-generating
dependency entailment. This makes it possible to formulate necessary and
sufficient conditions for the applicability of our techniques as constraint
validity problems. We characterize the computational complexity of such
problems
Preference Elicitation in Prioritized Skyline Queries
Preference queries incorporate the notion of binary preference relation into
relational database querying. Instead of returning all the answers, such
queries return only the best answers, according to a given preference relation.
Preference queries are a fast growing area of database research. Skyline
queries constitute one of the most thoroughly studied classes of preference
queries. A well known limitation of skyline queries is that skyline preference
relations assign the same importance to all attributes. In this work, we study
p-skyline queries that generalize skyline queries by allowing varying attribute
importance in preference relations. We perform an in-depth study of the
properties of p-skyline preference relations. In particular,we study the
problems of containment and minimal extension. We apply the obtained results to
the central problem of the paper: eliciting relative importance of attributes.
Relative importance is implicit in the constructed p-skyline preference
relation. The elicitation is based on user-selected sets of superior (positive)
and inferior (negative) examples. We show that the computational complexity of
elicitation depends on whether inferior examples are involved. If they are not,
elicitation can be achieved in polynomial time. Otherwise, it is NP-complete.
Our experiments show that the proposed elicitation algorithm has high accuracy
and good scalabilit
Efficient Skyline Querying with Variable User Preferences on Nominal Attributes
Current skyline evaluation techniques assume a fixed ordering on the
attributes. However, dynamic preferences on nominal attributes are more
realistic in known applications. In order to generate online response for any
such preference issued by a user, we propose two methods of different
characteristics. The first one is a semi-materialization method and the second
is an adaptive SFS method. Finally, we conduct experiments to show the
efficiency of our proposed algorithms.Comment: 10 page
Database Querying under Changing Preferences
We present here a formal foundation for an iterative and incremental approach
to constructing and evaluating preference queries. Our main focus is on query
modification: a query transformation approach which works by revising the
preference relation in the query. We provide a detailed analysis of the cases
where the order-theoretic properties of the preference relation are preserved
by the revision. We consider a number of different revision operators: union,
prioritized and Pareto composition. We also formulate algebraic laws that
enable incremental evaluation of preference queries. Finally, we consider two
variations of the basic framework: finite restrictions of preference relations
and weak-order extensions of strict partial order preference relations.Comment: Submitted to a journa
Crowdsourcing Pareto-Optimal Object Finding by Pairwise Comparisons
This is the first study on crowdsourcing Pareto-optimal object finding, which
has applications in public opinion collection, group decision making, and
information exploration. Departing from prior studies on crowdsourcing skyline
and ranking queries, it considers the case where objects do not have explicit
attributes and preference relations on objects are strict partial orders. The
partial orders are derived by aggregating crowdsourcers' responses to pairwise
comparison questions. The goal is to find all Pareto-optimal objects by the
fewest possible questions. It employs an iterative question-selection
framework. Guided by the principle of eagerly identifying non-Pareto optimal
objects, the framework only chooses candidate questions which must satisfy
three conditions. This design is both sufficient and efficient, as it is proven
to find a short terminal question sequence. The framework is further steered by
two ideas---macro-ordering and micro-ordering. By different micro-ordering
heuristics, the framework is instantiated into several algorithms with varying
power in pruning questions. Experiment results using both real crowdsourcing
marketplace and simulations exhibited not only orders of magnitude reductions
in questions when compared with a brute-force approach, but also
close-to-optimal performance from the most efficient instantiation
Continuous Monitoring of Pareto Frontiers on Partially Ordered Attributes for Many Users
We study the problem of continuous object dissemination---given a large
number of users and continuously arriving new objects, deliver an object to all
users who prefer the object. Many real world applications analyze users'
preferences for effective object dissemination. For continuously arriving
objects, timely finding users who prefer a new object is challenging. In this
paper, we consider an append-only table of objects with multiple attributes and
users' preferences on individual attributes are modeled as strict partial
orders. An object is preferred by a user if it belongs to the Pareto frontier
with respect to the user's partial orders. Users' preferences can be similar.
Exploiting shared computation across similar preferences of different users, we
design algorithms to find target users of a new object. In order to find users
of similar preferences, we study the novel problem of clustering users'
preferences that are represented as partial orders. We also present an
approximate solution of the problem of finding target users which is more
efficient than the exact one while ensuring sufficient accuracy. Furthermore,
we extend the algorithms to operate under the semantics of sliding window. We
present the results from comprehensive experiments for evaluating the
efficiency and effectiveness of the proposed techniques
User Profile-Driven Data Warehouse Summary for Adaptive OLAP Queries
Data warehousing is an essential element of decision support systems. It aims
at enabling the user knowledge to make better and faster daily business
decisions. To improve this decision support system and to give more and more
relevant information to the user, the need to integrate user's profiles into
the data warehouse process becomes crucial. In this paper, we propose to
exploit users' preferences as a basis for adapting OLAP (On-Line Analytical
Processing) queries to the user. For this, we present a user profile-driven
data warehouse approach that allows dening user's profile composed by his/her
identifier and a set of his/her preferences. Our approach is based on a general
data warehouse architecture and an adaptive OLAP analysis system. Our main idea
consists in creating a data warehouse materialized view for each user with
respect to his/her profile. This task is performed off-line when the user
defines his/her profile for the first time. Then, when a user query is
submitted to the data warehouse, the system deals with his/her data warehouse
materialized view instead of the whole data warehouse. In other words, the data
warehouse view summaries the data warehouse content for the user by taking into
account his/her preferences. Moreover, we are implementing our data warehouse
personalization approach under the SQL Server 2005 DBMS (DataBase Management
System)
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