Skyline analysis is important for multi-criteria decision making applications. The data in some of these applications are inherently uncertain due to various factors. Although a considerable amount of research has been dedicated separately to efficient skyline computation, as well as modeling uncertain data and answering some types of queries on uncertain data, how to conduct skyline analysis on uncertain data remains an open problem at large. In this thesis, we tackle the problem of skyline analysis on uncertain data. We propose a novel probabilistic skyline model where an uncertain object may take a probability to be in the skyline, and a p-skyline contains all the objects whose skyline probabilities are at least p. Computing probabilistic skylines on large uncertain data sets is challenging. An uncertain object is conceptually described by a probability density function (PDF) in the continuous case, or in the discrete case a set of instances (points) such that each instance has a probability to appear. We develop two efficient algorithms, the bottom-up and top-down algorithms, of computing p-skyline of a set of uncertain objects in the discrete case. We also discuss that our techniques can be applied to the continuous case as well. The bottom-up algorithm computes the skyline probabilities of some selected instances of uncertain objects, and uses those instances to prune other instances and uncertain objects effectively. The top-down algorithm recursively partitions the instances of uncertain objects into subsets, and prunes subsets and objects aggressively. Our experimental results on both the real NBA player data set and the benchmark synthetic data sets show that probabilistic skylines are interesting and useful, and our two algorithms are efficient on large data sets, and complementary to each other in performance.

De nos jours, nous assistons à l'émigration du Web de données vers le Web orienté services. L'amélioration des capacités et fonctionnalités des moteurs actuels de recherche sur le Web, par des techniques efficaces de recherche et de sélection de services, devient de plus en plus importante. Dans cette thèse, dans un premier temps, nous proposons un cadre de composition de services Web en tenant compte des préférences utilisateurs. Le modèle fondé sur la théorie des ensembles flous est utilisé pour représenter les préférences. L'approche proposée est basée sur une version étendue du principe d'optimalité de Pareto. Ainsi, la notion des top-k compositions est introduite pour répondre à des requêtes utilisateurs de nature complexe. Afin d'améliorer la qualité de l'ensemble des compositions retournées, un second filtre est appliqué à cet ensemble en utilisant le critère de diversité. Dans un second temps, nous avons considéré le problème de la sélection des services Web en présence de préférences émanant de plusieurs utilisateurs. Une nouvelle variante, appelée Skyline de services à majorité, du Skyline de services traditionnel est défini. Ce qui permet aux utilisateurs de prendre une décision " démocratique " conduisant aux services les plus appropriés. Un autre type de Skyline de services est également discuté dans cette thèse. Il s'agit d'un Skyline de Services de nature graduelle et se fonde sur une relation de dominance floue. Comme résultat, les services Web présentant un meilleur compromis entre les paramètres QoS sont retenus, alors que les services Web ayant un mauvais compromis entre les QoS sont exclus. Finalement, nous avons aussi absorbé le cas où les QoS décrivant les services Web sont entachés d'incertitude. La théorie des possibilités est utilisée comme modèle de l'incertain. Ainsi, un Skyline de Services possibilité est proposé pour permettre à l'utilisateur de sélectionner les services Web désirés en présence de QoS incertains. De riches expérimentations ont été conduites afin d'évaluer et de valider toutes les approches proposées dans cette thèse.

隨著巨量資料的議題逐漸被重視，有越來越多的巨量資料的分析都利用MapReduce作計算處理。而在資料庫查詢中，天際線查詢是一種常見的決策分析方法，其目的是要幫助使用者找出資料庫中各維度的數值貼近使用者查詢條件的資料。然而，過去在大量資料的查詢方法中，如果資料筆數較多，同時查詢的維度也大的情況下，往往會有著效率不彰的問題。因此，本研究提出一種在大量資料中，有效率應用MapReduce作天際線查詢的方法。而根據實驗結果顯示，我們的方法，比先前方法更有效率。 / With the big data issue being taken seriously today, more and more big data is processed with MapReduce. Moreover, skyline query is a common method for decision making, which helps users find the data whose value in each dimension is close to the user query. In the past, if the data is huge, or the data space involves many dimensions, the query processing becomes inefficient. Therefore, in this study, we present a new method to process skyline queries with MapReduce. According to the experimental results, our method is more efficient than previous methods.

Skyline analysis is important for multi-criteria decision making applications. The data in some of these applications are inherently uncertain due to various factors. Although a considerable amount of research has been dedicated separately to efficient skyline computation, as well as modeling uncertain data and answering some types of queries on uncertain data, how to conduct skyline analysis on uncertain data remains an open problem at large. In this thesis, we tackle the problem of skyline analysis on uncertain data. We propose a novel probabilistic skyline model where an uncertain object may take a probability to be in the skyline, and a p-skyline contains all the objects whose skyline probabilities are at least p. Computing probabilistic skylines on large uncertain data sets is challenging. An uncertain object is conceptually described by a probability density function (PDF) in the continuous case, or in the discrete case a set of instances (points) such that each instance has a probability to appear. We develop two efficient algorithms, the bottom-up and top-down algorithms, of computing p-skyline of a set of uncertain objects in the discrete case. We also discuss that our techniques can be applied to the continuous case as well. The bottom-up algorithm computes the skyline probabilities of some selected instances of uncertain objects, and uses those instances to prune other instances and uncertain objects effectively. The top-down algorithm recursively partitions the instances of uncertain objects into subsets, and prunes subsets and objects aggressively. Our experimental results on both the real NBA player data set and the benchmark synthetic data sets show that probabilistic skylines are interesting and useful, and our two algorithms are efficient on large data sets, and complementary to each other in performance.

Thesis (D. Miss.)--Western Seminary, Portland, OR, 2005. / Abstract. Includes bibliographical references (leaves 234-239).

Thesis (D. Miss.)--Western Seminary, Portland, OR, 2005. / Abstract. Includes bibliographical references (leaves 234-239).

Thesis (D. Miss.)--Western Seminary, Portland, OR, 2005. / Abstract. Includes bibliographical references (leaves 234-239).

No description available.

The capital of Germany Berlin will undergo a change in its skyline in the next few years.Due to a new housing policy that allows the construction of higher buildings, Berlinwill soon have several skyscrapers over 130 m high directly at Alexanderplatz as wellas next the Berlin landmark, the Berlin TV Tower.With the help of ArcGIS, the newbuildings are shown in 3D along with the impact of these buildings on the view to theTV Tower and the shadows cast by the new buildings on the Alexanderplatz. It is shownthat due to the new buildings, some areas around the Alexanderplatz no longer have acomplete view to the TV Tower. The shadow cast by the buildings will only slightlychange the current shadow cast on the Alexanderplatz, but neighbouring areas aroundthe Alexanderplatz will now be affected more by shadows.

Top-k and Skyline queries are a long study topic in database and information retrieval communities and they are two popular operations for preference retrieval. Top-k query returns a subset of the most relevant answers instead of all answers. Efficient top-k processing retrieves the k objects that have the highest overall score. In this paper, some algorithms that are used as a technique for efficient top-k processing for different scenarios have been represented. A framework based on existing algorithms with considering based cost optimization that works for these scenarios has been presented. This framework will be used when the user can determine the user ranking function. A real life scenario has been applied on this framework step by step. Skyline query returns a set of points that are not dominated (a record x dominates another record y if x is as good as y in all attributes and strictly better in at least one attribute) by other points in the given datasets. In this paper, some algorithms that are used for evaluating the skyline query have been introduced. One of the problems in the skyline query which is called curse of dimensionality has been presented. A new strategy that based on the skyline existing algorithms, skyline frequency and the binary tree strategy which gives a good solution for this problem has been presented. This new strategy will be used when the user cannot determine the user ranking function. A real life scenario is presented which apply this strategy step by step. Finally, the advantages of the top-k query have been applied on the skyline query in order to have a quickly and efficient retrieving results.