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Generating multidimensional schemata from relational aggregation queries

WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2020年第1期23卷 337-359页

作者： Huo, Zheng Taylor, Kerry Zhang, Xiuzhen Wang, Suzhen Pang, Chaoyi Hebei Univ Econ & Business Shijiazhuang Hebei Peoples R China CSIRO ICT Ctr Canberra ACT Australia RMIT Univ Melbourne Vic Australia Zhejiang Univ Hangzhou Peoples R China

Designing a multidimensional model is a non-trivial task: the requirements collected from senior managers can be inaccurate and difficult to use, especially when they are not database practitioners. On the other hand the pressing need for data warehousing comes from the fact that managers submit analytical summary queries against operational relational databases. In this paper, we view these analytical queries as a more reliable basis for multidimensional schema design. We first study how to use such queries to automatically generate measures, dimensions and dimension hierarchies and their representation in a star schema. We then use the results studying the schema evolution problem: If a new query Q(') can't be answered by the schema previously built, how to develop a new schema from the old one so that query Q(') can be answered in the new schema. Finally, we consider rewrite OLAP queries on conventional database (multidimensional data warehouse) using materialized views in data warehouse. In our method we analyze the attributes from the given relational queries and the dependency relationships among them to design a schema in which each dimension is a lattice. The schema can answer the queries that it was generated from and the number of dimensions is minimal. Furthermore, the schema can answer many more queries that are similar to the given ones.

关键词： Database view First-order logic incremental algorithm Maintenance SQL

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A incremental algorithm Based on Rough Set for Concept Hierarchy Tree

A Incremental Algorithm Based on Rough Set for Concept Hiera...

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4th International Conference on Graphic and Image Processing (ICGIP)

作者： Yuan, Junpeng Su, Jie Inst Sci & Tech Informat China Beijing Peoples R China Cent Univ Finance & Econ Sch Management Sci & Engn Beijing Peoples R China

ISBN: (纸本)9780819495662

In an open dynamic concept hierarchy tree of technological terms, universe keeps changing and then leads to changes in the system's structure and size characteristics. This study presents an efficient incremental algorithm based on rough set for maintaining the concept hierarchy tree in dynamic datasets. While taking into account the relationship between the new terms and the original concept hierarchy tree, the paper focus on when the condition attributes is known and the decision attributes is unknown, how to add a new term into the original concept hierarchy tree. The paper proposes a novel algorithm which can be used for updating concept hierarchy tree dynamically and proves the rationality of the algorithm theoretically. The paper has furthermore proved its efficiency and reliability with an empirical study of the Micro-Electro-Mechanical System, MEMS.

关键词： rough set concept hierarchy tree incremental algorithm data mining

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Tracking of multiple quantiles in dynamically varying data streams

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PATTERN ANALYSIS AND APPLICATIONS 2020年第1期23卷 225-237页

作者： Hammer, Hugo Lewi Yazidi, Anis Rue, Havard OsloMet Oslo Metropolitan Univ Dept Comp Sci Pilestredet 35 N-0166 Oslo Norway King Abdullah Univ Sci & Technol Thuwal Saudi Arabia

In this paper, we consider the problem of tracking multiple quantiles of dynamically varying data stream distributions. The method is based on making incremental updates of the quantile estimates every time a new sample is received. The method is memory and computationally efficient since it only stores one value for each quantile estimate and only performs one operation per quantile estimate when a new sample is received from the data stream. The estimates are realistic in the sense that the monotone property of quantiles is satisfied in every iteration. Experiments show that the method efficiently tracks multiple quantiles and outperforms state-of-the-art methods.

关键词： Dynamic environment incremental algorithm Multiple quantiles Quantile tracking

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DBSCAN-like clustering method for various data densities

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PATTERN ANALYSIS AND APPLICATIONS 2020年第2期23卷 541-554页

作者： Scitovski, Rudolf Sabo, Kristian Univ Osijek Dept Math Trg Ljudevita Gaja 6 Osijek 31000 Croatia

In this paper, we propose a modification of the well-known DBSCAN algorithm, which recognizes clusters with various data densities in a given set of data points A = {a(i) epsilon R-n : i = 1,..., m}. First, we define the parameter MinPts = vertical bar ln vertical bar A vertical bar vertical bar and after that, by using a standard procedure from DBSCAN algorithm, for each a epsilon A we determine radius epsilon(a) of the circle containing MinPts elements from the set.. We group the set of all these radii into the most appropriate number (t) of clusters by using Least Squares distance-like function applying SymDIRECT or SepDIRECT algorithm. In that way, we obtain parameters epsilon(1) >... > epsilon(t). Furthermore, for parameters {MinPts, epsilon(1)} we construct a partition starting with one cluster and then add new clusters for as long as the isolated groups of at least MinPts data points in some circle with radius epsilon(1) exist. We follow a similar procedure for other parameters epsilon(2),..., epsilon(t). After the implementation of the algorithm, a larger number of clusters appear than can be expected in the optimal partition. Along with defined criteria, some of them are merged by applying a merging process for which a detailed algorithm has been written. Compared to the standard DBSCAN algorithm, we show an obvious advantage for the case of data with various densities.

关键词： Clustering DBSCAN incremental algorithm Various data densities Clusters merging Least Squares distance-like function

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incremental maintenance of maximal cliques in a dynamic graph

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VLDB JOURNAL 2019年第3期28卷 351-375页

作者： Das, Apurba Svendsen, Michael Tirthapura, Srikanta Iowa State Univ Ames IA 50011 USA

We consider the maintenance of the set of all maximal cliques in a dynamic graph that is changing through the addition or deletion of edges. We present nearly tight bounds on the magnitude of change in the set of maximal cliques when edges are added to the graph, as well as the first change-sensitive algorithm for incremental clique maintenance under edge additions, whose runtime is proportional to the magnitude of the change in the set of maximal cliques, when the number of edges added is small. Our algorithm can also be applied to the decremental case, when edges are deleted from the graph. We present experimental results showing these algorithms are efficient in practice and are faster than prior work by two to three orders of magnitude.

关键词： Graph mining Maximal clique incremental algorithm Dynamic graph

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incremental approaches for feature selection from dynamic data with the variation of multiple objects

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KNOWLEDGE-BASED SYSTEMS 2019年第Jan.1期163卷 320-331页

作者： Shu, Wenhao Qian, Wenbin Xie, Yonghong East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Jiangxi Peoples R China Jiangxi Agr Univ Sch Software Nanchang 330045 Jiangxi Peoples R China Beijing Key Lab Knowledge Engn Mat Sci Beijing 100083 Peoples R China

Owing to the dynamic characteristics of data in the big data era, multiple objects of a decision system often vary with time when new information arrives in real-world applications. However, many feature selection algorithms are designed for static decision systems, and some dynamic feature selection algorithms treat the variation of multiple objects as the cumulative variation of a single object. In an environment where multiple objects vary with time, these algorithms are often time-consuming. Therefore, strategic behaviors need to be reinforced to improve the efficiency of feature selection. incremental updating is an efficient technique, which can be applied to deal with dynamic learning tasks because it can make use of previous knowledge to obtain new knowledge. In this paper, we focus on the incremental updating to select a new feature subset with the variation of multiple objects. First, the dependency function is updated in an incremental manner to evaluate the quality of candidate features. Then two incremental feature selection algorithms are developed when multiple objects are added to or deleted from a decision system. Experiments on different UCI data sets show that the proposed algorithms can select new feature subset in much less computational time and do not lose the classification performance when compared with other algorithms. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Feature selection Attribute reduction incremental algorithm Rough sets Dynamic data

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A Study on Rapid incremental Maximum Flow algorithm in Dynamic Network 1

A Study on Rapid Incremental Maximum Flow Algorithm in Dynam...

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1st International Cognitive Cities Conference (IC3)

作者： Wang, Yuanyuan Ling, Jianhui Zhou, Sihai Liu, Yundi Liao, Weicheng Zhang, Baili Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Southeast Univ Sch Foreign Languages Nanjing Jiangsu Peoples R China Supreme Count China Res Ctr Judicial Big Data Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538650592

Maximum flow is a key measurement for the capacity of a flow network When malfunction or damage occurs in branches of a dynamic network, it is urgent in many applications to identify whether the damaged network is still able to transfer demanded flow d. Obviously, recalculating maximum flow in the new network with the traditional algorithm is not a recommended solution for its higher time-complexity. As is known, in many existing maximum flow algorithms, the search for augmenting paths is critical but highly time-consuming. Thus, this paper proposes an incremental maximum flow algorithm based on augmented routing free (IMFA _ ART), directly producing augmenting paths without complex calculation. The algorithm caches the information of simple paths during calculating maximum flow in the original network The cached data will be utilized to get the augmenting paths whenever network topology changes, without any complex calculation in residual network In addition, in order to avoid traversing those invalid simple paths that contain saturated edges, an augmented routing free is proposed to index all simple paths. With the aid of this free, the next augmenting paths can be found sequentially to achieve maximum flow by traversing from the root to a leaf. The time complexity is determined by the height of ART, which is far less than the number of augmenting paths. Therefore, the algorithm performance can be improved significantly. Experiments show that IMFA_ART has greater advantage in time performance over Dinic, the most famous maximum flow algorithm.

关键词： maximum flow incremental algorithm augmented routing tree

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An incremental Map Matching algorithm Based on Weighted Shortest Path 18th

An Incremental Map Matching Algorithm Based on Weighted Shor...

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18th International Conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Chen, Jixiao Yang, Yongjian Huang, Liping Zhu, Zhuo Yang, Funing Jilin Univ Changchun Peoples R China

ISBN: (纸本)9783030050511;9783030050504

GPS (global position system) trajectories collected by urban cars depict the citizens' daily trips and reflect the traffic situation in city areas. The process called map matching is to match the GPS point sequence to the corresponding road which is the fundamental step for further travel pattern mining and transport situation analysis. The existing research based on the incremental map matching applies only to GPS trajectories of high-sampling-rate (0 to 30 s). However most actually collected GPS trajectories are with a low-sampling-rate (more than 2 min) for saving the collection and transmission costs. In this paper, we proposed an incremental map matching algorithm based on weighted shortest path, called WSI-matching. By matching single GPS point to its candidate road and filling the missing path between two GPS points, it improves the overall matching accuracy with a relatively low time complexity compared to the traditional global matching algorithm. Experiment results show that our WSI-matching algorithm present obvious advantages over traditional incremental algorithms and global algorithms in terms of both matching accuracy and running time, and it adapts to either high-sampling-rate trajectories or low-sampling-rate trajectories.

关键词： Map matching GPS trajectory Low-sampling-rate incremental algorithm Shortest path

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incremental solver for orbital-free density functional theory

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JOURNAL OF COMPUTATIONAL CHEMISTRY 2019年第23期40卷 2013-2027页

作者： Rousse, Francois Redon, Stephane Univ Grenoble Alpes INRIA CNRS Grenoble INPLJK F-38000 Grenoble France

First-principle calculations are still a challenge since they require a great amount of computational time. In this article, we introduce a new algorithm to perform orbital-free density functional theory (OF-DFT) calculations. Our new algorithm focuses computational efforts on important parts of the particle system, which, in the context of adaptively restrained particle simulations (ARPS) allows us to accelerate particle simulations. (c) 2019 Wiley Periodicals, Inc.

关键词： ab-initio molecular dynamic orbital-free DFT real-space finite-differences adaptively restrained particle simulations incremental algorithm

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Coordinate-Descent Adaptation over Hamiltonian Multi-Agent Networks

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SENSORS 2021年第22期21卷 7732-7732页

作者： Khalili, Azam Vahidpour, Vahid Rastegarnia, Amir Farzamnia, Ali Kin, Kenneth Teo Tze Sanei, Saeid Malayer Univ Dept Elect Engn Malayer *** Iran Univ Malaysia Sabah Fac Engn Kota Kinabalu 88400 Sabah Malaysia Nottingham Trent Univ Sci & Technol Clifton Lane Nottingham NG11 8NS England

The incremental least-mean-square (ILMS) algorithm is a useful method to perform distributed adaptation and learning in Hamiltonian networks. To implement the ILMS algorithm, each node needs to receive the local estimate of the previous node on the cycle path to update its own local estimate. However, in some practical situations, perfect data exchange may not be possible among the nodes. In this paper, we develop a new version of ILMS algorithm, wherein in its adaptation step, only a random subset of the coordinates of update vector is available. We draw a comparison between the proposed coordinate-descent incremental LMS (CD-ILMS) algorithm and the ILMS algorithm in terms of convergence rate and computational complexity. Employing the energy conservation relation approach, we derive closed-form expressions to describe the learning curves in terms of excess mean-square-error (EMSE) and mean-square deviation (MSD). We show that, the CD-ILMS algorithm has the same steady-state error performance compared with the ILMS algorithm. However, the CD-ILMS algorithm has a faster convergence rate. Numerical examples are given to verify the efficiency of the CD-ILMS algorithm and the accuracy of theoretical analysis.

关键词： adaptive estimation coordinate-descent distributed networks incremental algorithm

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