Services in cloud computing can be categorized into two groups: Application services and Utility Computing Services. Compositions in the application level are similar to the Web service compositions ...in SOC (Service-Oriented Computing). Compositions in the utility level are similar to the task matching and scheduling in grid computing. Contributions of this paper include: 1) An extensible QoS model is proposed to calculate the QoS values of services in cloud computing. 2) A genetic-algorithm-based approach is proposed to compose services in cloud computing. 3) A comparison is presented between the proposed approach and other algorithms, i.e., exhaustive search algorithms and random selection algorithms.
Map inference algorithm aims to construct a digital map from other data sources automatically. Due to the labour intensity of traditional map creation and the frequent road change nowadays, map ...inference is deemed to be a promising solution to automatic map construction and update. However, existing map inference from GPS trajectories suffers from low GPS data quality, which makes the quality of the constructed map unsatisfactory. In this paper, we study the existing map inference algorithms using GPS trajectories. Different from previous surveys, we (1) include the most recent solutions and propose a new categorisation of method; (2) study how different types of GPS errors affect the quality of inference results; (3) evaluate the existing map inference quality measures regarding their ability to identify map quality issues. To achieve these goals, we conduct a comprehensive experimental study on several representative algorithms using both real-world datasets and synthetic datasets, which are generated from our proposed synthetic trajectory generator and artificial map generator. Overall, our study provides insightful observations regarding (1) which inference method performs better in each working scenario, (2) the general data quality requirements for map inference, (3) the direction of future works for quantitative map quality measures.
Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the ...models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods.
Understanding short texts is crucial to many applications, but challenges abound. First, short texts do not always observe the syntax of a written language. As a result, traditional natural language ...processing tools, ranging from part-of-speech tagging to dependency parsing, cannot be easily applied. Second, short texts usually do not contain sufficient statistical signals to support many state-of-the-art approaches for text mining such as topic modeling. Third, short texts are more ambiguous and noisy, and are generated in an enormous volume, which further increases the difficulty to handle them. We argue that semantic knowledge is required in order to better understand short texts. In this work, we build a prototype system for short text understanding which exploits semantic knowledge provided by a well-known knowledgebase and automatically harvested from a web corpus. Our knowledge-intensive approaches disrupt traditional methods for tasks such as text segmentation, part-of-speech tagging, and concept labeling, in the sense that we focus on semantics in all these tasks. We conduct a comprehensive performance evaluation on real-life data. The results show that semantic knowledge is indispensable for short text understanding, and our knowledge-intensive approaches are both effective and efficient in discovering semantics of short texts.
Discovering popular routes from trajectories Zaiben Chen; Heng Tao Shen; Xiaofang Zhou
2011 IEEE 27th International Conference on Data Engineering,
2011-April
Conference Proceeding
The booming industry of location-based services has accumulated a huge collection of users' location trajectories of driving, cycling, hiking, etc. In this work, we investigate the problem of ...discovering the Most Popular Route (MPR) between two locations by observing the traveling behaviors of many previous users. This new query is beneficial to travelers who are asking directions or planning a trip in an unfamiliar city/area, as historical traveling experiences can reveal how people usually choose routes between locations. To achieve this goal, we firstly develop a Coherence Expanding algorithm to retrieve a transfer network from raw trajectories, for indicating all the possible movements between locations. After that, the Absorbing Markov Chain model is applied to derive a reasonable transfer probability for each transfer node in the network, which is subsequently used as the popularity indicator in the search phase. Finally, we propose a Maximum Probability Product algorithm to discover the MPR from a transfer network based on the popularity indicators in a breadth-first manner, and we illustrate the results and performance of the algorithm by extensive experiments.
A novel numerical scheme based on the Bell wavelets is proposed to obtain numerical solutions of the fractional integro-differential equations with weakly singular kernels. Bell wavelets are first ...proposed and their properties are studied, and the fractional integration operational matrix is constructed. The convergence analysis of Bell wavelets approximation is discussed. The fractional integro-differential equations can be simplified to a system of algebraic equations by using a truncated Bell wavelets series and the fractional operational matrix. The proposed method’s efficacy is supported via various examples.
Discovery of convoys in trajectory databases Jeung, Hoyoung; Yiu, Man Lung; Zhou, Xiaofang ...
Proceedings of the VLDB Endowment,
08/2008, Letnik:
1, Številka:
1
Journal Article
Recenzirano
Odprti dostop
As mobile devices with positioning capabilities continue to proliferate, data management for so-called trajectory databases that capture the historical movements of populations of moving objects ...becomes important. This paper considers the querying of such databases for convoys, a convoy being a group of objects that have traveled together for some time.
More specifically, this paper formalizes the concept of a convoy query using density-based notions, in order to capture groups of arbitrary extents and shapes. Convoy discovery is relevant for real-life applications in throughput planning of trucks and carpooling of vehicles. Although there has been extensive research on trajectories in the literature, none of this can be applied to retrieve correctly exact convoy result sets. Motivated by this, we develop three efficient algorithms for convoy discovery that adopt the well-known filter-refinement framework. In the filter step, we apply line-simplification techniques on the trajectories and establish distance bounds between the simplified trajectories. This permits efficient convoy discovery over the simplified trajectories without missing any actual convoys. In the refinement step, the candidate convoys are further processed to obtain the actual convoys. Our comprehensive empirical study offers insight into the properties of the paper's proposals and demonstrates that the proposals are effective and efficient on real-world trajectory data.
Best Keyword Cover Search Deng, Ke; Li, Xin; Lu, Jiaheng ...
IEEE transactions on knowledge and data engineering,
2015-Jan.-1, 2015-1-1, Letnik:
27, Številka:
1
Journal Article
Recenzirano
It is common that the objects in a spatial database (e.g., restaurants/hotels) are associated with keyword(s) to indicate their businesses/services/features. An interesting problem known as Closest ...Keywords search is to query objects, called keyword cover, which together cover a set of query keywords and have the minimum inter-objects distance. In recent years, we observe the increasing availability and importance of keyword rating in object evaluation for the better decision making. This motivates us to investigate a generic version of Closest Keywords search called Best Keyword Cover which considers inter-objects distance as well as the keyword rating of objects. The baseline algorithm is inspired by the methods of Closest Keywords search which is based on exhaustively combining objects from different query keywords to generate candidate keyword covers. When the number of query keywords increases, the performance of the baseline algorithm drops dramatically as a result of massive candidate keyword covers generated. To attack this drawback, this work proposes a much more scalable algorithm called keyword nearest neighbor expansion (keyword-NNE). Compared to the baseline algorithm, keyword-NNE algorithm significantly reduces the number of candidate keyword covers generated. The in-depth analysis and extensive experiments on real data sets have justified the superiority of our keyword-NNE algorithm.
With the advances in geo-positioning technologies and location-based services, it is nowadays quite common for road networks to have textual contents on the vertices. Previous work on identifying an ...optimal route that covers a sequence of query keywords has been studied in recent years. However, in many practical scenarios, an optimal route might not always be desirable. For example, a personalized route query is issued by providing some clues that describe the spatial context between PoIs along the route, where the result can be far from the optimal one. Therefore, in this paper, we investigate the problem of clue-based route search (CRS), which allows a user to provide clues on keywords and spatial relationships. First, we propose a greedy algorithm and a dynamic programming algorithm as baselines. To improve efficiency, we develop a branch-and-bound algorithm that prunes unnecessary vertices in query processing. In order to quickly locate candidate, we propose an AB-tree that stores both the distance and keyword information in tree structure. To further reduce the index size, we construct a PB-tree by utilizing the virtue of 2-hop label index to pinpoint the candidate. Extensive experiments are conducted and verify the superiority of our algorithms and index structures.
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