A user-friendly framework for database preferences

Preference queries are considered as a major necessity tool in today's database management system (DBMS). Adopting preference queries in the database application systems enable users to determine more than one objective in the submitted query which result into more accurate results compared to the traditional queries. Preference queries prefer one data item (tuple) p over the other data item (tuple) q if and only if p is better than q in all dimensions (attributes) and not worse than q in at least one dimension (attribute). Several preference evaluation techniques for preference queries have been proposed which aimed at finding the "best" results that meet the user preferences. These include but not limited to top-k, skyline, ranked skylines, k-representative dominance, k-dominance, top-k dominating, and k-frequency. This paper attempts to survey and analyze the following preference evaluation techniques of query processing in database systems: top-k, skyline, top-k dominating, kdominance, and k-frequency by highlighting the strengths and the weaknesses of each technique.

To handle the overwhelming amount of information currently available, personalization systems allow users to specify the information that interests them through preferences. In general, users may have different preferences depending on context, for instance, on the current weather, time or location. In this paper, we define a model for expressing contextual preferences. We model context as an ordered set of multidimensional attributes. Then, user preferences can be specified as functions over these context attributes. We formulate the problem of identifying the preferences that are most relevant to a query and present an algorithm that locates them. We also introduce index structures that exploit contextual information for (a) storing preferences and (b) caching the results of queries based on their context.

ABSTRACT 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.

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.

ACM Transactions on Database Systems, 2010

People's preferences are expressed at varying levels of granularity and detail as a result of partial or imperfect knowledge. One may have some preference for a general class of entities, e.g., liking comedies, and another one for a ne-grained, specic class, e.g., disliking recent thrillers with Al Pacino that are intended for families. In this paper, we are interested in capturing such complex, multi-granular preferences to personalize database queries and in studying their impact on query results. In particular, we organize the collection of one's preferences in a preference network (a directed acyclic graph), where each node refers to a subclass of the entities that its parent refers to, and whenever they both apply, more specic preferences override more generic ones. We study query personalization based on networks of preferences and provide ecient algorithms for identifying relevant preferences, modifying queries accordingly, and processing these queries to obtain personalized answers. Finally, we present results of both synthetic and realuser experiments, which (a) demonstrate the eciency of our algorithms, (b) provide insight as to the appropriateness of the proposed preference model and (c) show the benets of query personalization based on composite preferences compared to simpler preference representations.

21st International Conference on Data Engineering (ICDE'05), 2005

Query Personalization is the process of dynamically enhancing a query with related user preferences stored in a user profile with the aim of providing personalized answers. The underlying idea is that different users may find different things relevant to a search due to different preferences. Essential ingredients of query personalization are: (a) a model for representing and storing preferences in user profiles, and (b) algorithms for the generation of personalized answers using stored preferences. Modeling the plethora of preference types is a challenge. In this paper, we present a preference model that combines expressivity and concision. In addition, we provide efficient algorithms for the selection of preferences related to a query, and an algorithm for the progressive generation of personalized results, which are ranked based on user interest. Several classes of ranking functions are provided for this purpose. We present results of experiments both synthetic and with real users (a) demonstrating the efficiency of our algorithms, (b) showing the benefits of query personalization, and (c) providing insight as to the appropriateness of the proposed ranking functions.

2008 Third International Conference on Digital Information Management, 2008

A key characteristic of emerging OLAP database systems will be customizability of their behaviour taking into account users preferences as well as their context of analysis. In this paper we define a personalization framework for OLAP database systems based on user context-aware preferences. We consider a qualitative preference model which handles user preferences on the multidimensional schema. Context is modelled as a tree of multidimensional components of an OLAP analysis (fact, measures, dimensions, parameters). We define some OLAP operations that support personalization. User queries are dynamically enhanced with his preferences and are aware of the analysis context.

2007 IEEE 23rd International Conference on Data Engineering Workshop, 2007

User-defined preferences allow personalized ranking of query results. A user provides a declarative specification of his/her preferences, and the system is expected to use that specification to give more prominence to preferred answers. We study constraint formalisms for expressing user preferences as base facts in a partial order. We consider a language that allows comparison and a limited form of arithmetic, and show that the transitive closure computation required to complete the partial order terminates. We consider various ways of composing partial orders from smaller pieces, and provide results on the size of the resulting transitive closures. Finally, we show how preference queries within our language can be supported by suitable index structures for efficient evaluation over large data sets. Our results provide guidance about when complex preferences can be efficiently evaluated, and when they cannot.

Proceedings of the 11th international conference on Extending database technology Advances in database technology - EDBT '08, 2008

To provide users with only relevant data from the huge amount of available information, personalization systems utilize preferences to allow users to express their interest on specific pieces of data. Most often, user preferences vary depending on the circumstances. For instance, when with friends, users may like to watch thrillers, whereas, when with their kids, they may prefer to watch cartoons. Contextual preference systems address this challenge by supporting preferences that depend on the values of contextual attributes such as the surrounding environment, time or location. In this paper, we address the problem of finding interesting data items based on contextual preferences that assign interest scores to pieces of data based on context. To this end, we propose a number of pre-processing steps. Instead of pre-computing scores for all data items under all potential context states, we exploit the hierarchical nature of context attributes to identify representative context states. Furthermore, we introduce a method for grouping preferences based on the similarity of the scores that they produce. This method uses a bitmap representation of preferences and scores with various levels of precision that lead to approximate rankings with different degrees of accuracy. We evaluate our approach using both real and synthetic data sets and present experimental results showing the quality of the scores attained using our methods.

Proceedings. 20th International Conference on Data Engineering, 2004

As information becomes available in increasing amounts to a wide spectrum of users, the need for a shift towards a more user-centered information access paradigm arises. We develop a personal-ization framework for database systems based on user profiles and identify the ...