Earticle

The vehicle monitoring system is a very important class of information systems. However, the existing monitoring systems generally require the support of the large database systems and the monitoring event could not be processed in real time. The research of internet of vehicles has proposed the novel ideas and methods to build the monitoring system. In this paper, we construct a vehicle monitoring system based on a complex event query language: STeCEQL. Firstly, we give the approach of verifying the reasonableness of the monitoring events through building the model of the vehicles. Then we propose the method of describe the vehicle monitoring events by STeCEQL. Meanwhile, we give the complex event detection algorithms based on the event tree. Finally, the simulation results demonstrate the effectiveness of the vehicle monitoring approach of internet of vehicles.

Without multi query optimization, Relational Database Management System for online and analytical decision support systems would have been inefficient and hence unpractical. It is an expensive process because it relies at a great extent on evaluating the different plans (access paths) and choosing an optimal one among them. In Multi Query Optimization, queries are executed in batches and there were many different algorithms acted in such way that, in case some queries have a common sub-expression such a sub- expression is executed once and the output shared. We studied the basic multi query optimization algorithms including Basic Volcano, Volcano-SH and Volcano RU, identified their strengths and weaknesses and recommend strategies for developing new improved multi query optimization algorithm so as to reduce weaknesses and integrate strengths of the different basic multi query algorithms into one efficient algorithm.

This article studied weibo text representation. For the weibo features such as short, real-time, colloquialism and originality, in the original vector space model, we propose a suitable method for weibo text representation. Make all the content words as feature words after participation. And we proposed T-TFIDF weight calculation method according to the features of weibo. According to the vector space model, we proposed a weibo adaptive topic tracking methods based on K-means clustering. Simulation analysis shows that, the method can by comparing the similarity micro-blog and sub topic vector set, determine whether weibo belonging to the topic.

Mapping RDB to RDF (i.e., RDB2RDF) is the key to constructing the Semantic Web, hence has been an active research field during the last decade. Many technically heterogeneous RDB2RDF tools resulted in non-interchangeable and unreusable RDB2RDF mapping descriptions. In 2009, the W3C RDB2RDF Incubator Group Report once strongly suggested that the RDB2RDF mapping language be expressed in rules as defined by the W3C Rule Interchange Format (RIF) Working Group, because rules are an effective way to express mappings between information models, and RIF, as part of the infrastructure for the Semantic Web, is now a standard for exchanging rules among Web rule systems. This paper addresses the issue of RIF-based RDB2RDF mapping and proposes a database semantics-driven, RIF Production Rule Dialect (RIF-PRD) based mapping description approach. The work includes defining a set of generic RIF-PRD mapping rules for RDB2RDF, developing a prototype mapping engine called RIFD2RME (stands for RIF-based RDB2RDF Mapping Engine), and conducting case study experiments with the prototype. The experimental results indicate that the proposed mapping approach is achievable and effective.

Examining data to find similar data is a major problem in data mining and information retrieval. There are abundant documents that contain information. Most of those documents are duplicates or near duplicates and they increase storage space and cost time for searching for information needed. Reduction of dimensionality and well organization of data are the ways that can be used to solve the problem of efficiency. In this paper we proposed a method based mined frequent terms from each document to reduce the data size and efficient method for clustering documents that have close similarity between them. Using our method only 36.4% of original size has been used. The similarity between documents is based on frequent terms shared. Our method performs well on running time of O(n) whereas the current methods for clustering require O(n3).

To increase directional query efficiency, based on the study of existing algorithms of directional query, new pruning rules for directional query were given, combined with the new index structure MB-tree. The rules exclude the MBRs outside the query area and output all leaf nodes in the MBRs inside the query area. Based on the orders defined in MB-tree, a new algorithm of directional query is given combining with MB-tree by using recursive method, and the new algorithm can reduce I/O cost effectively. Experiment showed that the new directional query algorithm reduces the number of visited nodes, decreases I/O cost, and improves the efficiency of directional query.

Cloud services provide platform for multiple users to remotely store , share and update data items. Mobile virtual environment and effective use of cloud computing made this great success. . This paper is an implemented real time web and mobile application named BookCafé using cloud database storage (common for both web and mobile) provided by Amazon web service. BookCafé is a storage where all the books will be stored with year wise and subject wise classification for student’s purpose. Update’s can be done at any time. They can have like laptops, smart phones, and tablet computer devices. Users of Online web application (non smart) access through user interface for signing, reading and uploading new books category wise. By uploading the android application on Google Play store, smart phone users can avail all the facilities on their handheld devices. The students either through mobile (android based) or through web, share and upload new books from anywhere in the campus by connecting to cloud.

With the reverse engineering development, the accuracy of system is more important in reconstruction, especially in non-contacting measurement. This paper provides a new method measuring the accuracy the point clouds, define a image probability and the point probability according to uncertainty data. The quantity of the uncertain point data is important to measuring the result of reconstruction. The prior data can be catch from the last measurement process, especially the edge data or characteristic points. Referring to prior data, basing on the Bayesian theory the more accuracy posterior data can be computed in this paper. We divided the point cloud into different areas, and organized the data with hierarchical tree-structure. According to the probability of one tree node, we adjust the area corresponding to the node. At last, by using the existing experimental equipment, we verify the measurement of point cloud accuracy algorithm. The depth data was obtained by a laser scanner---SICK LMS100. The depth data can be computed as point data with uncertainty. The result of the reconstruction deeply relies on the quality of prior data.

Several lock based protocols were introduced to increase the concurrency of a tree based XML structure. Concurrency can be increased when locking of resources is done at finer granular level. When the locking is done at the finest granular level like at node, more number of users can access the same document simultaneously. In the existing systems, all operations are done using only one of the API’s like SAX, DOM or XPath. For this a new compatibility matrix is proposed which includes all the operations possible to an XML document in order to increase the concurrent access to the same XML document and also to lock the resources to the finest granular level.

Stencils are finite-difference algorithms for solving large-scale and high-dimension partial differential equations. Due to the data dependences among the iterative statements in Stencils, traditional Stencil computations are be executed serially, rather than in parallel. It’s challenging to design an effective and scalable Stencil parallelized method. To address the issue of 3D data space computing, we present a serial execution model based on multi-layers symmetric Stencil method and time skewing techniques. Within this model, the iteration space is divided to multiple tiles based on time skewing, where the executive process is ordered by the sequence of tiles, and the nodes in each individual tile can be swept repeatedly to improve the data locality. In addition, we propose a novel 3D iterative space alternate tiling Stencil parallel method, which subdivides the iteration space along high dimension, and changes the execution sequence of tiles to reduce the data dependency and communication cost, where the partial order of tiles is still guaranteed. Experimental results demonstrate our proposed alternative tiling parallel method achieves better parallel efficiency and scalability compared with the domain-decomposition methods.

One of the key advances in resolving the “big-data” problem has been the emergence of an alternative database technology. Today, classic RDBMS are complemented by a rich set of alternative Data Management Systems (DMS) specially designed to handle the volume, variety, velocity and variability of Big Data collections; these DMS include NoSQL, NewSQL and Search-based systems. NewSQL is a class of modern relational database management systems (RDBMS) that provide the same scalable performance of NoSQL systems for online transaction processing (OLTP) read-write workloads while still maintaining the ACID guarantees of a traditional database system. This paper discusses about NewSQL data management system; and compares with NoSQL and with traditional database system. This paper covers architecture, characteristics, classification of NewSQL databases for online transaction processing (OLTP) for Big data management. It also provides the list of popular NoSQL as well as NewSQL databases in separate categorized tables. This paper compares SQL based RDBMS, NoSQL and NewSQL databases with set of metrics; as well as, addressed some research issues of NoSQL and NewSQL.

This paper describes the core functional components and a visualization module implementation process of visual data mining technology prototype system. The first part introduces the implementation technology of visual data mining technology prototype system and then describes the overall design of the architecture and features of visual data mining technology prototype system; Then paper describes the common components in the visualization of data mining technology prototype system. Research achievements of this article provides a useful reference to optimize visual data mining techniques.

Cloud storage enables users to outsource their data to cloud servers and enjoy the on-demand services. However, this new paradigm also introduces integrity threats toward user’ outsourced data. This paper develops an efficient auditing mechanism, which support batch auditing for multiple data files in multi-cloud environment. By constructing a sequence-enforced Merkle Hash Tree, the proposed protocol can resist the replace attack. By using the bilinear map, the proposed protocol achieves stateless and transparent verification. By putting the computation of intermediate values of the verification on cloud servers, our method can greatly reduce the computing burden of the auditor. The performance analysis proves the good efficiency of the proposed protocol.

Now a large scale of data every day, the large-scale data is usually in the form of database storage. The law of the people wants to find useful or knowledge, thus was born the Data Mining technology. SVM (Support Vector Machine, SVM) is a very useful method in data mining, this paper mainly discusses the Support Vector Machine (SVM) play a key role in nuclear techniques and the selection of model parameters is analyzed and evaluated. This article some methods about how to construct the kernel function is introduced for the model to find suitable kernel function is to provide some reference strategies and proposed kernel function method for the simulation analysis.

Rough K-means algorithm has shown that it can provides a reasonable set of lower and upper bounds for a given dataset. With the conceptions of the lower and upper approximate sets, rough k-means clustering and its emerging derivatives become valid algorithms in vague information clustering. However, the most available algorithms ignore the difference of the distances between data objects and cluster centers when computing new mean for each cluster. To solve this issue, an improved algorithm of rough k-means clustering based on variable weighted distance measure is presented in this article. Comparative experimental results of real world data from UCI demonstrate the validity of the proposed algorithm.

There are rich data resources in materials science, but these data resources are heterogeneous in level of system, structure, syntax, and semantics. Therefore, a domain ontology is necessary and helpful for the integration of these heterogeneous data resources, and it is also one of the main tasks of materials informatics. In this paper, we propose a steel semantic model (named STSM) based on ontology and logic rules for the representation of the steel knowledge. STSM is developed with the consideration of the features of materials data and the developed process is presented. Then, we describe the content and organization of STSM which covers the basic knowledge in steel domain. Further, domain axioms and logic rules are designed to enhance the reasoning ability of STSM. STSM is built and tested in protégé, and an experimental prototype based on Jena is also developed to demonstrate the effectiveness of STSM.

Currently, most sentiment analysis of microblog has been focused on coarse-grained sentiment analysis, but fine-grained sentiment is better for reflecting the opinion of the public when they are facing the social focus. Therefore, a hybrid strategy which is a combination of Naïve Bayesian and two-layer CRFs is put forward, which has been applied to the fine-grained sentiment analysis of Chinese microblog. First, microblog is classified into two types: sentiment and non-sentiment by using Naïve Bayesian classification algorithm. And then the first-layer CRFs model is built for the topic emotional sentence. Finally CRFs algorithm is used again to do multi-classification to assign a specific sentiment category. Experimental results show that a good result in sentiment identification based on the combination of Naïve Bayesian and CRFs, and also show the advantage of the combination of Naïve Bayesian and CRFs interrelated with emotional sentence extraction based on CRFs.

Word sense disambiguation (WSD) is important to many application problems in natural language processing fields, such as machine translation, parsing analysis and information retrieval. In this paper, we propose a new method to determine correct sense categories of Chinese words based on linguistics knowledge. The left word string and the right word string around the ambiguous word are respectively analyzed. Their syntactic structures are obtained for determining its intended sense. Syntactic category and part of speech are extracted as disambiguation features. A naive bayesian model is used as the classifier. Experimental results showed that the accuracy rate of classification arrives at 64%. The performance of disambiguation is improved.

With the development of our national sport consciousness, sports are a more and more important role in people's daily life. Community sports have been adopted by more and more people. But due to historical and economic reasons, community sport level in China is quite low. How to conduct an evaluation of the community life sport, not only is the community residents request, but also an important means to make corresponding improvement for a city builder. However, due to the mathematical algorithm has high complexity for the general case, applicants to environmental assessment is difficult, In this paper, introduce the C4.5 and improved DRNN as machine learning methods, and data mining the existing community life sport, to construct the evaluate model of community life sport , Not only can make up for the algorithm less shortcoming of current community sport research, but also can get the objective conclusion, to evaluate the mechanism of factors, and to give counsel for the development of community sport.

Cloud is a heterogeneous group of services and one of that is data storage. Generally the data stored in cloud is in very large amount, retrieval of the same requires precision and accuracy. Many methods are being proposed to retrieve data from cloud and strict match algorithm is one of them. In this paper we use of Levenshtein distance to find out the amount of similarity between two different strings. We compare the keyword given by user with our set of words, find out similarity and provide guesses to the user as accurate as possible.

Text clustering is the method of combining text or documents which are similar and dissimilar to one another. In several text tasks, this text mining is used such as extraction of information and concept/entity, summarization of documents, modeling of relation with entity, categorization/classification and clustering. This text mining categorizes only digital documents or text and it is a method of data mining. It is the method of combining text document into category and applied in various applications such as retrieval of information, web or corporate information systems. Clustering is also called unsupervised learning because like other document classification, no labeled documents are providing in clustering; hence, clustering is also known as unsupervised learning. A new method called Hierarchical Agglomerative Clustering (HAC) which manages clusters as tree like structure that make possible for browsing. In this HAC method, the nodes in the tree can be viewed as parent-child relationship i.e. topic-subtopic relationship in a hierarchy. HAC method starts with each example in its own cluster and iteratively combines them to form larger and larger clusters. The main focus of this work is to present a performance analysis of various techniques available for document clustering.