Preference Queries Research Articles

Incremental elicitation of Choquet capacities for multicriteria choice, ranking and sorting problems

This paper proposes incremental preference elicitation methods for multicriteria decision making with a Choquet integral. The Choquet integral is an evaluation function that performs a weighted aggregation of criterion values using a capacity function assigning a weight to any coalition of criteria, thus enabling positive and/or negative interactions among them and covering an important range of possible decision behaviors. However, the specification of the capacity involves many parameters which raises challenging questions, both in terms of elicitation burden and guarantee on the quality of the final recommendation.In this paper, we investigate the incremental elicitation of the capacity through a sequence of preference queries (questions) selected one-by-one using a minimax regret strategy so as to progressively reduce the set of possible capacities until the regret (the worst-case “loss” due to reasoning with only partially specified capacities) is low enough. We propose a new approach designed to efficiently compute minimax regret for the Choquet model and we show how this approach can be used in different settings: 1) the problem of recommending a single alternative, 2) the problem of ranking alternatives from best to worst, and 3) sorting several alternatives into ordered categories. Numerical experiments are provided to demonstrate the practical efficiency of our approach for each of these situations.

RegisPt: Registry of Registries in Portugal – Design, Data Model, and Functionalities

Introduction: The number and quality of existing patient registries are not known in Portugal. In order to improve the knowledge regarding this issue, an interactive tool (RegisPt) was developed to identify and characterize all available patient registries in our country. This article aims to describe the RegisPt design, data model, and due functionalities. Methods: RegisPt was developed in Microsoft Office Access 2010 and all variables definitions were done according to standardized international classifications. A review of the available literature and web resources was performed in order to identify all relevant patient registries. Results: The RegisPt platform is divided into 5 core modules containing comprehensive information from each patient registry (general data, registry design, characterization, effectiveness, and publications). Effectiveness is of utmost importance for health technology assessment and is divided into 2 sections: the exposure (health care service, pharmaceutical drugs, and medical devices) and the outcomes (safety, clinical, and economic). The RegisPt platform allows adding and editing registries as well as consulting all available registries in an interactive and user-friendly way, using a preferred query (e.g., registry name, institution, and therapeutic area). About 50 patient registries were identified and characterized in Portugal in accordance with the patient registry definition of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Discussion: The RegisPt is a first step to better understand and use the available resources for health research in Portugal. RegisPt can promote collaboration between researchers and registry owners and encourage the efficient use of resources and may improve the access to registries. This project may also be useful to identify therapeutic areas still lacking patient registries and to optimize existing ones in the country, by comparing registry design, quality, and functional strategies. In the future, this valuable platform may be particularly relevant for researchers and authorities aiming to carry out health technology assessment.

Skyline Preference Query Based on Massive and Incomplete Dataset

Personalized recommendation and the processing of real-time data exemplify the processing of massive data which in the field of Internet-of-Things (IoT) received a great extent of attention in recent literature. The incompleteness of massive data in the IoT is widespread. Obtaining personalized information from the incomplete data set is still puzzled by searching efficient and accurate methods at present. Skyline query is a widely used data processing method, especially in the field of multi-objective decision analysis and data visualization. To eliminate the negative effects on massive data processing in IoT, a novel skyline preference query strategy based on massive and the incomplete data set is proposed in this paper. This strategy simply separates and divides massive and incomplete data set into two parts according to dimension importance and executes skyline query, respectively. The strategy mainly resolves the problem of extracting personalized information from massive and incomplete data set and improves the efficiency of skyline query on massive and incomplete data set. First, this paper presents a skyline preference query strategy based on strict clustering and implements it on dimensions that have higher importance. Second, a skyline preference query strategy based on loose clustering is implemented on dimensions that have lower importance. Finally, integrating local skyline query results, this paper calculates global skyline query results by using information entropy theory. The efficiency and effectiveness of Skyline Preference Query (SPQ) algorithm have been evaluated in terms of response time and result set size through the comparative experiments with ISkyline algorithm and sort-based incomplete data skyline algorithm. A large number of simulation results show that the efficiency of SPQ algorithm is higher than that of other common methods.

Searching Similar Books Based on Student’s Preference for Personalized Education

Personalized education aims to give students a personalized learning schedule according to students’ backgrounds and preferences, and the required learning resources for learning are personalized. On-line bookstore allows students to collect learning recourses on-line through Internet, but the problem of information overload plagues students since it is difficult to find the suitable books with the data becoming diverse and massive. Similarity search aims to find the similar objects to a given query, which can be regarded as a promising solution to the problem of information overload. However, the existing similarity search approaches limit the query into only one object, the students cannot express their preferences personally. In this paper, we proposed a personalized similarity search framework, towards finding the similar books based on student’s preference for personalized education. We build the student-book network based on the students’ ratings for books, and use SimRank to measure the similarities between books according to the student-book network. For satisfying student’s personalized query preference, we allow student to express query with multi-books. A personalized similarity measure is proposed for measuring the similarity between query and candidate book by combining the similarities between books. Experiments on Amazon dataset demonstrate that, when the number of input books are not limited into one, the returned rankings are more consistent with students’ query intentions.

Top-k Spatial Preference Queries in Directed Road Networks

Top-k spatial preference queries rank objects based on the score of feature objects in their spatial neighborhood. Top-k preference queries are crucial for a wide range of location based services such as hotel browsing and apartment searching. In recent years, a lot of research has been conducted on processing of top-k spatial preference queries in Euclidean space. While few algorithms study top-k preference queries in road networks, they all focus on undirected road networks. In this paper, we investigate the problem of processing the top-k spatial preference queries in directed road networks where each road segment has a particular orientation. Computation of data object scores requires examining the scores of each feature object in its spatial neighborhood. This may cause the computational delay, thus resulting in a high query processing time. In this paper, we address this problem by proposing a pruning and grouping of feature objects to reduce the number of feature objects. Furthermore, we present an efficient algorithm called TOPS that can process top-k spatial preference queries in directed road networks. Experimental results indicate that our algorithm significantly reduces the query processing time compared to period solution for a wide range of problem settings.

S i 2 p

The incomplete data is universal in many real-life applications due to data integration, the limitation of devices, etc. In this demonstration, we present S i 2 p , a restaurant recommendation <u>S</u>ystem with <u>P</u>reference queries on <u>I</u>ncomplete <u>I</u>nformation. S i 2 p is capable of friendly recommending desirable restaurants based on preference queries that take the incomplete ratings information into consideration. It adopts the browser-server model, and incorporates three functionality modules including friendly and convenient query submission , flexible and useful result explanation , timely and incremental dataset interaction. S i 2 p provides the server side based on an extended PostgreSQL database that integrates two types of preference queries, namely, skyline and top- k dominating queries over incomplete data. It also offers the browser-based interface for the users to interact with the system. Using a real restaurant dataset from TripAdvisor, we demonstrate S i 2 p can recommend and explore the restaurants in a friendly way.

K-dominant skyline queries on incomplete data

The skyline query has been extensively explored as one of popular techniques to filter uninteresting data objects, which plays an important role in many real-life applications such as multi-criteria decision making and personalized services. This query has also been incorporated into commercial database systems for supporting preference queries. However, a skyline query may retrieve too many objects to analyze intensively especially for high-dimensional datasets. As a result, k-dominant skyline query has been introduced to control the number of the objects retrieved. Existing algorithms for k-dominant skyline queries only aim at complete data, which is not well-suited for incomplete data, even though incomplete data is pervasive in scientific research and real life, due to delivery failure, no power of battery, accidental loss, etc. In this paper, we systematically study the problem of k-dominant skyline queries on incomplete data (IkDS), where the data objects might miss their attribute values. We formalize the IkDS query and then present three efficient algorithms for finding k-dominant skyline objects over incomplete data. Several novel concepts/techniques are utilized including local skyline, dominance ability, and bitmap index on incomplete data to shrink the search space. In addition, we extend our techniques to tackle two interesting variants, i.e., weighted dominant skyline query and top-δ dominant skyline query, over incomplete data. Extensive experiments using both real and synthetic data sets demonstrate the performance of our proposed algorithms.

Learning Market Parameters Using Aggregate Demand Queries

We study efficient algorithms for a natural learning problem in markets. There is one seller with m divisible goods and n buyers with unknown individual utility functions and budgets of money. The seller can repeatedly announce prices and observe aggregate demand bundles requested by the buyers. The goal of the seller is to learn the utility functions and budgets of the buyers. Our scenario falls into the classic domain of ''revealed preference'' analysis. Problems with revealed preference have recently started to attract increased interest in computer science due to their fundamental nature in understanding customer behavior in electronic markets. The goal of revealed preference analysis is to observe rational agent behavior, to explain it using a suitable model for the utility functions, and to predict future agent behavior. Our results are the first polynomial-time algorithms to learn utility and budget parameters via revealed preference queries in classic Fisher markets with multiple buyers. Our analysis concentrates on linear, CES, and Leontief markets, which are the most prominent classes studied in the literature. Some of our results extend to general Arrow-Debreu exchange markets.

Incremental Quality Based Reverse Ranking for Spatial Data

Background/Objectives : The scope of this proposed work is to minimize the search time and complexity in spatial database. Methods: Improves the query processing in spatial database by using the existing R Tree, IR Tree and Reverse Ranking. A comparative analysis is made between the existing methods and the proposed method Incremental Quality Reverse Ranking (IQRR). The proposed method effectively evaluates to find the top-k spatial objects in multiple query processing. Findings: To evaluate the performance of the proposed approach, a comparative study has been performed in this work. The R tree and IR tree are compared with the proposed work namely Incremental Quality Reverse Ranking (IQRR). The evaluation parameters are radius, time, location, directions and number of dams. Applications: A spatial preference query ranks objects (e.g. Dams) based on the qualities of features (irrigation, water supply, flood control, hydro electricity, navigation, recreation, and pollution control) in their spatial neighborhood. In future, according to the user specification, it may be developed for any spatial network application. This application can be deployed in the cloud server and cloud will provide a service to the user.

Top-$k$ Preferences in High Dimensions

Given a set of objects $\mathcal {O}$ , each with $d$ numeric attributes, a top- $k$ preference scores these objects using a linear combination of their attribute values, where the weight on each attribute reflects the interest in this attribute. Given a query preference $q$ , a top- $k$ query finds the $k$ objects in $\mathcal {O}$ with highest scores with respect to $q$ . Given a query object $o$ and a set of preferences $\mathcal {Q}$ , a reverse top- $k$ query finds all preferences $q\in \mathcal {Q}$ for which $o$ becomes one of the top $k$ objects with respect to $q$ . Previous solutions to these problems are effective only in low dimensions. In this paper, we develop a solution for much higher dimensions (up to high tens), if many preferences exhibit sparsity —i.e., each specifies non-zero weights for only a handful (say $5$ - $7$ ) of attributes (though the subsets of such attributes and their weights can vary greatly). Our idea is to select carefully a set of low-dimensional core subspaces to “cover” the sparse preferences in a workload. These subspaces allow us to index them more effectively than the full-dimensional space. Being multi-dimensional, each subspace covers many possible preferences; furthermore, multiple subspaces can jointly cover a preference, thereby expanding the coverage beyond each subspace’s dimensionality. Experimental evaluation validates our solution’s effectiveness and advantages over previous solutions.

Representing and Reasoning with Qualitative Preferences: Tools and Applications

Abstract This book provides a tutorial introduction to modern techniques for representing and reasoning about qualitative preferences with respect to a set of alternatives. The syntax and semantics of several languages for representing preference languages, including CP-nets, TCP-nets, CI-nets, and CP-theories, are reviewed. Some key problems in reasoning about preferences are introduced, including determining whether one alternative is preferred to another, or whether they are equivalent, with respect to a given set of preferences. These tasks can be reduced to model checking in temporal logic. Specifically, an induced preference graph that represents a given set of preferences can be efficiently encoded using a Kripke Structure for Computational Tree Logic (CTL). One can translate preference queries with respect to a set of preferences into an equivalent set of formulae in CTL, such that the CTL formula is satisfied whenever the preference query holds. This allows us to use a model checker to reason abo...

Sliding window top-k dominating query processing over distributed data streams

Preference query processing is important for a wide range of applications involving distributed databases, such as network monitoring, web-based systems, and market analysis. In such applications, data objects are generated frequently and massively, which presents an important and challenging problem of continuous query processing over distributed data stream environments. A top-k dominating query, which has been receiving much research attention recently, returns the k data objects that dominate the highest number of data objects in a given dataset, and due to its dominance-based ranking function, we can easily obtain superior data objects. An emerging requirement in distributed stream environments is an efficient technique for continuously monitoring top-k dominating data objects. Despite of this fact, no study has addressed this problem. In this paper, therefore, we address the problem of continuous top-k dominating query processing over distributed data stream environments. We present two algorithms that monitor the exact top-k dominating data and efficiently eliminate unqualified data objects for the result, which reduces both communication and computation costs. In addition to these algorithms, we present an approximate algorithm that further reduces both communication and computation costs. Extensive experiments on both synthetic and real data have demonstrated the efficiency and scalability of our algorithms.

Bayesian Preference Elicitation for Multiobjective Engineering Design Optimization

Many engineering optimization problems have more than one objective. Although there are methods for identifying the Pareto front of a design space, these methods can be computationally prohibitive, and it is still left to the designer to choose which design on the front to build. Consequently, multiobjective optimization often involves specifying a scalar utility function and then optimizing with respect to this utility function. Preference elicitation algorithms can aid the designer in constructing this utility function. In preference elicitation for engineering design optimization, the ideal algorithm would converge to an accurate utility function with as few preference queries to the designer as possible. This paper extends a prior method for preference realization and tailors it for use in engineering design optimization by deriving a less restrictive inference technique and applying an entropy-based method for posing queries that converges faster to the true global utility function than existing algorithms. This method is then applied to optimization of a next-generation aircraft collision-avoidance system. Finally, an implementation of the algorithm is released as an open-source package in the Julia language.

SPARQL queries on RDF with fuzzy constraints and preferences

With the widespread acceptance of RDF (Resource Description Framework) as the de-facto standard recommended by W3C (World Wide Web Consortium) for the representation and exchange of information on the Web, a huge amount of RDF data is being proliferated and becoming available. Efficient querying of RDF data is therefore of increasing importance. Preference queries and fuzzy queries are required for personalized queries, and have been extensively investigated in the context of databases. Focusing on SPARQL (Simple Protocol and RDF Query Language), we propose a fuzzy extension of SPARQL with preferences in this paper. First, we extend SPARQL with fuzzy sets by allowing the occurrence of fuzzy terms, fuzzy operators and fuzzy relations. Second, we introduce multi-dimensional preferences into the fuzzy SPARQL. Based on fuzzy set theory, we also propose a set of translation rules to convert the fuzzy SPARQL with preferences into crisp SPARQL. Finally we propose an effective scoring function to calculate the order of query results, which integrates the preferences and membership degrees. The proposed approach is evaluated with experiments as well.

Efficient processing of top-k dominating queries in distributed environments

Due to the recent massive data generation, preference queries are becoming an increasingly important for users because such queries retrieve only a small number of preferable data objects from a hu...

Erratum to: Database preference queries - a possibilistic logic approach with symbolic priorities

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 counter-example 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.

Crowdsourcing for Query Processing onWeb Data: A Case Study on the Skyline Operator

In recent years, crowdsourcing has become a powerful tool to bring human intelligence into information processing. This is especially important forWeb data which in contrast to well-maintained databases is almost always incomplete and may be distributed over a variety of sources. Crowdsourcing allows to tackle many problems which are not yet attainable using machine-based algorithms alone: in particular, it allows to perform database operators on incomplete data as human workers can be used to provide values during runtime. As this can become costly quickly, elaborate optimization is required. In this paper, we showcase how such optimizations can be performed for the popular skyline operator for preference queries. We present some heuristics-based approaches and compare them to crowdsourcing-based approaches using sophisticated optimization techniques while especially focusing on result correctness.

A New Top-k Conditional XML Preference Queries

Preference querying technology is a very important issue in a variety of applications ranging from ecommerce to personalized search engines. Most of recent research works have been dedicated to this topic in the Artificial Intelligence and Database fields. Several formalisms allowing preference reasoning and specification have been proposed in the Artificial Intelligence domain. On the other hand, in the Database field the interest has been focused mainly in extending standard Structured Query Language (SQL) and also eXtensible Markup Language (XML) with preference facilities in order to provide personalized query answering. More precisely, the interest in the database context focuses on the notion of Top-k preference query and on the development of efficient methods for evaluating these queries. A Top-k preference query returns k data tuples which are the most preferred according to the user’s preferences. Of course, Top-k preference query answering is closely dependent on the particular preference model underlying the semantics of the operators responsible for selecting the best tuples. In this paper, we consider the Conditional Preference queries (CP-queries) where preferences are specified by a set of rules expressed in a logical formalism. We introduce Top-k conditional preference queries (Top-k CP-queries), and the operators BestK-Match and Best-Match for evaluating these queries will be presented.

Preference Elicitation and Interview Minimization in Stable Matchings

While stable matching problems are widely studied, little work has investigated schemes for effectively eliciting agent preferences using either preference (e.g., comparison) queries for interviews (to form such comparisons); and no work has addressed how to combine both. We develop a new model for representing and assessing agent preferences that accommodates both forms of information and (heuristically) minimizes the number of queries and interviews required to determine a stable matching. Our Refine-then-Interview (RtI) scheme uses coarse preference queries to refine knowledge of agent preferences and relies on interviews only to assess comparisons of relatively “close” options. Empirical results show that RtI compares favorably to a recent pure interview minimization algorithm, and that the number of interviews it requires is generally independent of the size of the market.

Continuous Probabilistic Subspace Skyline Query Processing Using Grid Projections

As an important type of multidimensional preference query, the skyline query can find a superset of optimal results when there is no given linear function to combine values for all attributes of interest. Its processing has been extensively investigated in the past. While most skyline query processing algorithms are designed based on the assumption that query processing is done for all attributes in a static dataset with deterministic attribute values, some advanced work has been done recently to remove part of such a strong assumption in order to process skyline queries for real-life applications, namely, to deal with data with multi-valued attributes (known as data uncertainty), to support skyline queries in a subspace which is a subset of attributes selected by the user, and to support continuous queries on streaming data. Naturally, there are many application scenarios where these three complex issues must be considered together. In this paper, we tackle the problem of probabilistic subspace skyline query processing over sliding windows on uncertain data streams. That is, to retrieve all objects from the most recent window of streaming data in a user-selected subspace with a skyline probability no smaller than a given threshold. Based on the subtle relationship between the full space and an arbitrary subspace, a novel approach using a regular grid indexing structure is developed for this problem. An extensive empirical study under various settings is conducted to show the effectiveness and efficiency of our PSS algorithm.