Flexible and extensible preference evaluation in database systems

Abstract

Personalized database systems give users answers tailored to their personal preferences. While numerous preference evaluation methods for databases have been proposed (e.g., skyline, top-k, k-dominance, kfrequency), the implementation of these methods at the core of a database system is a double-edged sword. Core implementation provides efficient query processing for arbitrary database queries, however this approach is not practical since each existing (and future) preference method requires implementation within the database engine. To solve this problem, this paper introduces FlexPref, a framework for extensible preference evaluation in database systems. FlexPref, implemented in the query processor, aims to support a wide-array of preference evaluation methods in a single extensible code base. Integration with FlexPref is simple, involving the registration of only three functions that capture the essence of the preference method. Once integrated, the preference method "lives" at the core of the database, enabling the efficient execution of preference queries involving common database operations. This paper also provides a query optimization framework for FlexPref, as well as a theoretical framework that defines the properties a preference method must exhibit to be implemented in FlexPref. To demonstrate the extensibility of FlexPref, this paper also provide case studies detailing the implementation of seven state-of-the-art preference evaluation methods within FlexPref. We also experimentally study the strengths and weaknesses of an implementation of FlexPref in PostgreSQL over a range of single -table and multi-table preference queries.

Exact Dominance Querying Algorithm on CP-nets

“…Further research as follows: (1) studying some other query task, such as skyline query, Top-k query on CP-nets [17]. (2) studying how to efficient store all the preference relation N as shown in literature [42].…”

“…Concurrently, within the database community, some work have been done to integrate preference handling into database query languages like preference SQL, which are built on a variety of preference constructors under a strict partial order semantics. Levandoski introduces a framework for extensible preference evaluation in database systems, and aims to support a wide-array of preference evaluation methods in a single extensible code base [17]. Beyond all that, some sophisticated algebraic optimization techniques and preference queries evaluation algorithms have been developed, for example, Endres attempts to transform queries over TCP-net (the extension of CP-nets) model into database preference queries systematically [18].…”

A secured context-aware tourism recommender system using artificial bee colony and simulated annealing

“…Liu (2009) discussed an effective method to rank items reducing the searching time. A contextual query-based recommendation technique has been proposed by Levandoski et al (2013). Dutta and Kumaravel (2015) designed a method to incorporate contextual information which increases the recommender performance.…”

Expressive efficiency of two kinds of specific CP-nets

“…Given a CP-net, complexity issue is to determine the computational complexities of some query tasks, such as dominance test (checking whether an outcome is preferred to another one, that is, whether N o 1 1 o 2 is true [14]), outcome optimization query [3,27] (solving the best outcomes, that is, we look for the most preferable outcomes from outcome space X), skyline query [1] (determining outcomes which are not dominated by any other outcomes), and other query tasks as described in literature [22]. Indeed, all these query tasks can be perceived as reasoning tasks on graphical model, the complexity perspective is concerned with devising effective query algorithms for solving these reasoning tasks.…”