Preference rules in database querying

2008

The paper presents a new approach to database preferences queries, where preferences are represented in a possibilistic logic manner, using symbolic weights. The symbolic weights may be processed without assessing their precise value, which leaves the freedom for the user to not specify any priority among the preferences. The user may also enforce a (partial) ordering between them, if necessary. The approach can be related to the processing of fuzzy queries whose components are conditionally weighted in terms of importance. Here, importance levels are symbolically processed, and refinements of both Pareto ordering and minimum ordering are used. The representational power of the proposed setting is stressed, while the approach is compared with database Best operator-like methods and with the CP-net approach developed in artificial intelligence. The paper also provides a structured and rather broad overview of the different lines of research in the literature dealing with the handling of preferences in database queries.

Annals of Mathematics and Artificial Intelligence, 2011

This note corrects a claim made in the above-mentioned paper about the exact representation of a conditional preference network by means of a possibilistic logic base with partially ordered symbolic weights. We provide a counterexample that shows that the possibilistic logic representation is indeed not always exact. This is the basis of a short discussion on the difficulty of obtaining an exact representation. This note corrects a claim made in [6] about the representation of Conditional Preference networks (CP-nets for short) [1] by means of a possibilistic logic base [2], as well as a similar claim in [7, 8]. A CP-net encodes a set of preference statements concerning the values of Boolean decision variables, conditioned on the values of other Boolean decision variables that influence the former. More formally, let V = {X 1 , • • • , Xn} be a set of Boolean variables. We denote by Ast(S) the set of interpretations of variables of S (⊆ V). Definition 1 A CP-net N over V = {X 1 , • • • , Xn} is a directed graph with nodes X 1 , • • • , Xn, and there is a directed edge from X i to X j if the preference about the value X j depends on the value of X i. Each node X i ∈ V is associated with a conditional preference table CP T i that associates a strict preference (x i > ¬x i or ¬x i > x i) with each possible instantiation u j i ∈ Ast(P a(X i)) of the parents P a(X i) of X i (if any). Each entry in a conditional preference table CP T i is of the form φ = u j i : j x i > j ¬x i , where u j i ∈ Ast(P a(X i)), j is blank if the preference is x i > ¬x i and is ¬ otherwise. Each interpretation ω, i.e., an instantiation of all variables in V , is understood as a solution to the decision problem described by the preference statements in the CP-net. A CP-net induces a partial preference ordering over solutions defined as follows. Each

ACM Transactions on Database Systems, 2011

Preferences have been traditionally studied in philosophy, psychology and economics and applied to decision making problems. Recently, they have attracted the attention of researchers in other fields, such as databases where they capture soft criteria for queries. Databases bring a whole fresh perspective to the study of preferences, both computational and representational. From a representational perspective, the central question is how we can effectively represent preferences and incorporate them in database querying. From a computational perspective, we can look at how we can efficiently process preferences in the context of database queries. Several approaches have been proposed but a systematic study of these works is missing. The purpose of this survey is to provide a framework for placing existing works in perspective and highlight critical open challenges to serve as a springboard for researchers in database systems. We organize our study around three axes: preference representation, preference composition and preference query processing.

IEEE Transactions on Knowledge and Data Engineering, 2014

In this paper, we argue that preference-aware query processing needs to be pushed closer to the DBMS. We introduce a preference-aware relational data model that extends database tuples with preferences and an extended algebra that captures the essence of processing queries with preferences. Based on a set of algebraic properties and a cost model that we propose, we provide several query optimization strategies for extended query plans. Further, we describe a query execution algorithm that blends preference evaluation with query execution, while making effective use of the native query engine. We have implemented our framework and methods in a prototype system, PrefDB. PrefDB allows transparent and efficient evaluation of preferential queries on top of a relational DBMS. Our extensive experimental evaluation on two real-world datasets demonstrates the feasibility and advantages of our framework.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013

The study of preferences has a long tradition in many disciplines, but it has only relatively recently entered the realm of data management through their application in answering queries to relational databases. The current revolution in data availability through the Web and, perhaps most importantly in the last few years, social media sites and applications, puts ontology languages at the forefront of data and information management technologies. In this paper, we propose the first (to our knowledge) integration of ontology languages with preferences as in relational databases by developing PrefDatalog+/-, an extension of the Datalog+/-family of languages with preference management formalisms closely related to those previously studied for relational databases. We focus on two kinds of answers to queries that are relevant to this setting, skyline and krank (a generalization of top-k queries), and develop algorithms for computing these answers to DAQs (disjunctions of atomic queries) as well as preliminary algorithms for CQs (conjunctive queries) based on related work in the databases and ontologies literature. We show that DAQ answering in PrefDatalog+/-can be done in polynomial time in the data complexity, as in relational databases, as long as query answering can also be done in polynomial time (in the data complexity) in the underlying classical ontology.

2012

In this demonstration we present a preference-aware relational query answering system, termed PrefDB. The key novelty of PrefDB is the use of an extended relational data model and algebra that allow expressing different flavors of preferential queries. Furthermore, unlike existing approaches that either treat the DBMS as a black box or require modifications of the database core, PrefDB's hybrid implementation enables operator-level query optimizations without being obtrusive to the database engine. We showcase the flexibility and efficiency of PrefDB using PrefDBAdmin, a graphical tool that we have built aiming at assisting application designers in the task of building, testing and tuning queries with preferences.

Lecture Notes in Computer Science

Preferences can be used for information filtering and extraction to deliver the most relevant data to the user. Therefore the efficient integration of querying with preferences into standard database technology is an important issue. The paper resumes a logical framework for formulating preferences and their embedding into relational algebra through a single preference operator parameterized by a set of user preferences of sixteen various kinds and returning only the most preferred subsets of its argument relation. Most importantly, preferences between sets of elements can be expressed. To make a relational query language with the preference operator useful for practical applications, formal foundation for algebraic optimization, applying heuristics like push preference, has to be provided. Therefore abstract properties of the preference operator and a variety of algebraic laws describing its interaction with other relational algebra operators are presented.

In this work we present a new model that combines two different types of preferences, qualitative and quantitative. We show how our model can support different types of preferences at different granularity levels and how an application can use these preferences to retrieve a list of tuples. The new model takes advantage of a graph representation of preferences where nodes in the graph are SQL predicates, edges between two nodes describe a qualitative preference and edges from a node to the same node capture quantitative preferences. Each edge is labeled with a numeric value, between -1 and 1, that expresses the intensity of each preference. Using this graph representation, we further show how two different preferences can be combined and applied to the existing query result set to filter the result and identify the most relevant tuples first.

1995

This research is concerned with the semantics, applications, and implementation of a declarative language for specifying constraint optimization and relaxation problems. These problems arise in diverse application areas|document layout, scheduling, parsing, etc. Existing constraint languages are ill-equipped to express such problems, and provide ad hoc constructs whose semantics is provided in an unsatisfactory manner. We propose a principled extension of constraint logic programming for solving these problems. Our proposed paradigm is called preference logic programming, and we have shown how to formulate constraint optimization problems as well as heuristics for hard combinatorial problems in this paradigm. We are presently investigating the kinds of constraint relaxation problems that can be naturally expressed in the paradigm. We also propose to explore the use of preferences in the grammatical and database contexts. We have developed an extension of logic grammars, called preference logic grammars, capable of expressing the criteria for disambiguating ambiguous parses. We introduce preference datalog programs as restricted preference logic programs with database applications in mind.

Proceedings of the fifth ACM SIGACT-SIGMOD symposium on Principles of database systems - PODS '86, 1986

We assume the reader familiar with the basic concepts of refational databases (Ul] and with the logical query language for databases; using PROLOG's notation, described in [U2]. A database logic query is expressed as a triple <G ,LP ,D >, where G is a goal to be solved using the rules of the logic program LP and the facts of the relational database D. In thii paper, we study the problem of efficient implementations of queries on recursive rules without function symbols. We focus on an important subclass, called canonical strongly linear queries (CSL queries), and study the binding-passing property, which entails the propagation of the initial bindings (established by constants in the query goal) during the top-down (as in backward chaining) execution phase. The paper is organized as follows. In Section 2, we define CSL queries and study the binding propagation problem. In Section 3, we focus on l-bound CSL queries, where the binding propagates to a single (but not always the same)'argument of the recursive predicate. In Section 4, we study the problem of implementing these queries. We use a unifying framework to provide a simple description of the following four methods: the counting method (informally described in p+]), the eager method (similar to that in [HN]), the magic set method (presented in [B+]), and a new method here introduced, called magic counting, which combines the advantages of the first and the third. Extensions to and proofs of these results are given in [SZ].