Earticle

Temporal data model ensures the correctness, validity and compability of data in temporal database, and temporal formal languages model describes the semantic specification of temporal database, both two are core and basis of temporal database system design and development. The paper has exploited the quo of temporal data models and temporal formal languages models, presented FTDM (Formal Temporal Data Model) and L (FTDM) over FTDM by denotation semantics methods. Referring to thinking of software reuse, this paper proposed the definition of languages reuse. Based on the previous works the authors further made temporal formal languages models family {Li} with its particular categorical properties analyzed in this paper by categorical methods, also demonstrated rigorously inherent relationships between temporal formal languages models in {Li}. Their works provided a category theoretic method with universality and flexible expansion for temporal formal languages models, meantime, proposed solid theoretical foundations for design and development of temporal database system.

Hadoop–a popular open-source implementation of MapReduce is widely used for the analysis of large datasets. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. In this paper we evaluate performance of Hadoop Platform and Oracle for Distributed Parallel Processing in large datasets. For evaluation, we implement a prototype of a virtual datacenter using distributed and parallel computing technology. The purpose of this paper is to reduce datacenter implementation cost using commodity hardware and provide high performance. Hadoop is installed on a commodity Linux cluster the distributed processing of large data sets across clusters of computers using distributed and parallel computing architecture. This paper also helps to explain about some new technology and framework which are open source; that can easily utilize those technologies for our complex data analysis which resembling structured, semi structured and non-structured data. Here we tried to demonstrate a performance comparison by executing some queries between distributed parallel computing system and traditional single computing system. For the simulation of the infrastructure Hadoop cluster has been used for distributed parallel processing and Oracle 11g is used for traditional single processing system. We prepare three virtual host for Hadoop cluster and a high-end hardware for Oracle 11g.

By studying the existing ontology design method and integrating the feature of domain knowledge which has significant component characteristics, this paper propose a method to build ontology knowledge base basing on structural member. The process includes the following six steps: requirement analysis, framework designing, coding, ontology evaluation, ontology evolution, document filing. Each step of the task is further decomposed into several detailed operation. An ontology evaluation method is also proposed. Through applying the domain ontology into the practices and evaluating the result before and after, the rationality of relationship is reversely inferred out. Ontology language is used to describe the concepts and the relation among them. It is also an ideal selection to describe the ancient buildings knowledge. The result of instant study confirms the approach is feasible and effective.

Automatic gender classification of an individual can be very useful in video-based surveillance systems and human-computer interaction systems. Currently, gait from a single viewpoint has been used to recognize the gender of a person. Considering the multiple cameras used in real environments, we investigate gender classification from human gait by using multi-view fusion, a relatively understudied problem. In this paper, we present a new approach to integrate information from multi-view gait at the feature level. First, gait energy images (GEI) are constructed from the video streams for different viewpoints. Then, the feature fusion is performed by putting GEI images and camera views together to generate a third-order tensor (x, y, view). A multi-linear principal component analysis (MPCA) is employed to reduce dimensionality of the tensor objects which integrate all views. The proposed fusion scheme is tested on CASIA database and compared with other fusion methods. The experimental results show that MPCA based feature fusion is quite effective for multi-view gait based gender classification.

Cluster examination is data mining task for the assignment of collection a set of items in such a path, to the point that questions in the same gathering (called a cluster) are more like one another than to those in different gatherings (clusters). K-means grouping is a technique for group investigation which intends to parcel n perceptions into k groups in which every perception fits in with the cluster with the closest mean. This paper, decided the aftereffect of standard parameter estimations of shading picture division with k-means and the modified k-means with ABC and ACO algorithms. The paper demonstrates that division of color picture with modified k-mean consolidated with swarm Intelligence calculations for color image segmentation gives preferable results over simple k-means and Modified k-means with Ant colony optimization gives better results than modified k-means with Artificial bee colony.

Twin support vector machine (TWSVM) was initially designed for binary classification. However, real-world problems often require the discrimination more than two categories. To tackle multi-class classification problem, in this paper, a multiple least squares twin support vector machine is proposed. Our Multi-LSTSVM solves K quadratic programming problems (QPPs) to obtain K hyperplanes, each problem is similar to binary LSTSVM. Comparison against the Multi-LSSVM, Multi-GEPSVM, Multi-TWSVM and our Multi-LSTSVM on both UCI datasets and ORL, YALE face datasets illustrate the effectiveness of the proposed method.

Support Vector Machine (SVM) is extremely powerful and widely accepted classifier in the field of machine learning due to its better generalization capability. However, SVM is not suiTable for large scale dataset due to its high computational complexity. The computation and storage requirement increases tremendously for large dataset. In this paper, we have proposed a MapReduce based SVM for large scale data. MapReduce is a distributed programming model which works on large scale dataset by dividing the huge datasets in smaller chunks. MapReduce distribution model works on several frame works like Hadoop Twister and so on. In this paper, we have analyzed the impact of penalty and kernel parameters on the performance of parallel SVM. The experimental result shows that the number of support vectors and predictive accuracy of SVM is affected by the choice of these parameters. From experimental results, it is also analyzed that the computation time taken by the SVM with multi-node cluster is less as compared to the single node cluster for large dataset.

In recent years, the rapid development of Internet of Things has received wide attention of the social and academic circles. However, if there is no unified standard to store and process the huge data, the systems are still highly independent and interconnection is difficult to be realized. This paper researches the design and implementation of data platform based on Internet of Things technology. We firstly analyze the data sources and features to understand the platform requirements. Then we propose the data platform scheme with the function and performance requirements considered. It focuses on the resource identification and addressing, resource description and management, data storage, processing and analysis problem. With the data platform, the resources in Internet of Things system are managed in a unified way, which improves the system openness, access and transmission capability thus makes the system more flexible and open. However, the current design scheme can be improved in performance and safety in the future research.

In the current era of big data, digital library (DL) is migrating towards the directions of semantic exchanges and service interactions to facilitate DL users to search, share and reuse the digital resources in a more effective and efficient manner. This paper explores Semantic Web technologies for ontology-based modeling of DL service metadata across ubiquitous DLs, enabling to add semantics to DL services to address issues related to representation, cooperation and accessibility of services in or across the communities. In particular, the DL service metadata ontology addresses the dynamic behavior of a DL service by nesting the stateful changes, constraint rules and mapping rules to achieve the dynamic coherence for seamless service interoperability in the service lifecycle. The operation of the prototype system is demonstrated to validate the implementation of the proposed approach through access and visualization in a usage scenario.

Data is considered as a new kind of strategic resource, which can dig out more potential value. The concept of agricultural big data has entered people's field of vision, and agricultural informatization plays an important role in promoting the optimization of agricultural industry. In this paper, we make analysis of industrial structure by using large-scale data. According to the grey correlation analysis, the result shows that correlation degree between planting and agriculture industry is higher, so it is very important to optimize the structure of agricultural industry in the northeast region. At the same time, we should make full use of information technology to improve the agricultural efficiency, improve the agricultural education and training system, and promote the development of modern agriculture.

Visualization technology now has been widely used in the field of seismic exploration, which can improve the quality and efficiency of data processing, reduce risk and cost of exploration. This paper mainly studies the visualization of 2D seismic trace data, and figures out the oscillograph of trace data based on the actual data. In the drawing of the oscillograph, to avoid the situation that oscillograph becomes polygonal line when dealing with fewer sampling points, this paper introduces Bezier Curve Fitting method, and as the test data demonstrate, the oscillograph becomes more smooth with the help of Bezier Curve Fitting, and the expected effects are achieved.

Query processing technology has recently received a lot of attention in the business intelligence and information service communities. However, the existing approaches can not efficiently optimize the query performance in the uncertain big data environment. In this paper, we propose QPPUBG, a novel and efficient query processing platform for uncertain big data. QPPUBG mainly includes four modules: (i) query equivalence reconstructing for uncertain big data; (ii) multiple query optimization over probability relation components; (iii) query execution plan constructing over probability relation components, and (iv) physical implementation solution of query for uncertain big data. Specially, QPPUBG can support the possible world instance semantics and efficiently handle arbitrary decision spaces. Moreover, QPPUBG can seamlessly integrate the above four modules into the modern parallel computation frameworks. We present the extensive experiments that demonstrate QPPUBG is both efficient and effective.

In recent years, the popularity of cloud computing technology is widely grown and most organizations want to use this technology in their business processes. But on the other hand, the use of this technology is not easy and many organizations are concerned about storing their sensitive data in their data centers instead of storing them in the cloud storage centers. In the cloud computing environment, trust, as a solution to enhance the security, has attracted the attention of researchers. Trust is one of the most important ways to improve the reliability of cloud computing resources provided in the cloud environment and has an important role in the business environments. Trusting the user to select the appropriate source helps in heterogeneous cloud infrastructure. In this paper, we present the trust model based on standards of appropriate service quality and speed of implementation for cloud resources. Simulation results show that the proposed model compared with similar models, in addition to taking into account measures of the quality of service, selects the most reliable source in a cloud environment by taking into account the speed of things.

In recent years, there has been widespread concern about the problems of stream data query both academic and industrial communities. The problems obtained some results. At the same time, big data stream brings great benefits for information society. Information query about stream data form has also brought crucial challenges. However, it is seldom about the research of big data stream query in network space. This paper analyzes the characteristics of stream data query in massive data, discusses the challenges and research issues of data stream for big data query. Finally the works for the data stream query are surveyed.

Recently, more and more undergraduates have turned Internet to find support and take part in society in campus. However, current SNS systems are not oriented to a specific campus. In this paper, we design an online multi-tenant social networking service in campus, including At Helper and At Society. At Helper aims at improving the success ratio of the help process with a multi-tenant architecture, and At Society provides social services using popular instant messaging mobile application. Both modules change the traditional operation and maintenance to rent services. Besides, the key innovative design, such as multi-tenancy and access-aware data cache, are discussed in detail.

For the premature convergence and initial pheromone distribution problem of ant colony optimization algorithm, an improved particle swarm optimization (MPSO) algorithm is introduced into ant colony optimization algorithm in order to propose a novel hybrid evolution optimization (HEACO) algorithm in this paper. In the proposed HEACO algorithm, the ergodicity of the chaos is used to initialize the swarm in order to enhance the diversity of the particle swarm, and adjust the mutation probability and inertia weighting factor in order to improve the capability of local and global search. Then the MPSO algorithm is used to control the parameters of the heuristic factor, pheromone evaporation coefficient, and the stochastic selection threshold in order to effectively overcome the parameter influences of ACO, reduce the numbers of useless experiments and balance the developing optimal solution and enlarging search space. A series of typical traveling salesman problems are selected to validity the effectiveness of the proposed HEACO algorithm. The simulation results show that the performance of the proposed HEACO algorithm is better than the traditional ACO algorithm and PSO algorithm. So the proposed HEACO algorithm can effectively improve the solving efficiency and quality, and speed up the convergence and computation.

Recent years, data mining has become a very popular concept in computer science. Its application has covered many crucial subjects. However, the systemic research based on relevant techniques in the field of chemistry is still non-existent. Here, we present a future possibility of the osmosis of data mining to chemistry under the circumstance of the age of big data, using artificial neural network (ANN) models as a crucial example. By presenting its applications in different research areas, this paper gives a comprehensive understanding to the ANN and its potential to dominate the chemical data mining area.

Automatic handwritten digits recognition is useful in a large variety of applications such as cheque verification and mail sorting. However, the selection of the technique for feature extraction remains the big challenge step for achieving high recognition accuracy. This paper presents a technique based on DWT and DCT to capture the discriminative features of handwritten digits. DCT coefficients are extracted from low-frequency sub-band (LL) of DWT image. These coefficients are fed into the ANN in the classification stage. This work has been tested with ADBase database containing 70,000 digits images, and a comparison made against some existing techniques, and promising results have been obtained.

In order to overcome the blindness of chaotic search, improve the convergence speed and global solving ability of the basic ant colony optimization(ACO) algorithm, an improved ACO algorithm based on combining multi-population strategy, adaptive adjustment pheromone strategy, chaotic search method and min-max ant strategy (MPCSMACO)is proposed in this paper. In the proposed MPCSMACO algorithm, the multi-population strategy is introduced to realize the information exchange and cooperation among the various types of ant colony. The chaotic search method with the ergodicity, randomness and regularity by using the logistic mapping is used to overcome too long search time, avoid falling into the local extremum in the initial stage and improve the search accuracy in the late search. The min-max ant strategy is used to avoid the local optimization solution and the stagnation. And the ants with different probability search different area according to the concentration of pheromone, so as to reduce the search number of the blindness of chaotic search method. Several Benchmark functions are selected to testify the performance of the MPCSMACO algorithm. The experiment results show that the MPCSMACO algorithm takes on the better global search ability and convergence performance.

The amount of digital information that is created and used is progressively rising along with the growth of sophisticated hardware and software. In addition, real-world data come in a diversity of forms and can be tremendously bulky. This has augmented the need for powerful algorithms that can deduce and dig out appealing facts and useful information from these data. Text Mining (TM), which is a very complex process; has been successfully used for this purpose. Text mining alternately referred to as text data mining, more or less equivalent to text analytics, can be defined as the process of extracting high-quality information from text. Text mining involves the process of structuring the input data, deriving patterns within the structured data and lastly interpretation and revelation of the output. This paper provides outline on text analytics and social media analytics. At the end, this paper presents our proposed work based on ontology framework to cope up with excessive social media textual data.

Hadoop Distributed File System (HDFS) becomes a representative cloud platform, benefiting from its reliable, scalable and low-cost storage capability. However, HDFS does not present good storage and access performance when processing a huge number of small files, because massive small files bring heavy burden on NameNode of HDFS. Meanwhile, HDFS does not provide any optimization solution for storing and accessing small files, as well as no prefetching mechanism to reduce I/O operations. This paper proposes an optimized scheme, Structured Index File Merging-SIFM, using two level file indexes, the structured metadata storage, and prefetching and caching strategy, to reduce the I/O operations and improve the access efficiency. Extensive experiments demonstrate that the proposed SIFM can effectively achieve better performance in the terms of the storing and accessing for a large number of small files on HDFS, compared with native HDFS and HAR.

With the rapid development of civil aviation and raising public awareness of environmental issues, it is extremely crucial to reduce airport noise impact in the vicinity of airports. Reduction of noise at source and operation restrictions are two prominent noise abatement approaches, both of which reduce aircraft noise impact by selecting appropriate aircraft types for flights. However, reduction of noise at source requires high cost while operation restrictions would restrict the ability of full operation of the airport. To tackle with the above issue, the paper studies the problem of aircraft type selection for noise reduction, the target of which is to select aircraft type with the lowest noise from a variety of candidate types. To this end, the paper employs weighted equivalent continuous perceived noise level to measure the noise level, and then utilizes Integrated Noise Model (INM) to calculate noise impact area at the interval of noise level. Experimental results on Beijing International Airport noise monitoring dataset show that the proposed algorithm can indeed obtain the aircraft type with minimal aircraft noise impact.

This article comprises a review of various sequential algorithms. The review represents the working nature of the learning methods. It also includes the methods such as Minimal Resource Allocation Network (MRAN), Extreme Learning Machine (ELM), Self-regulated Resource Allocation Network (SRAN) and Meta-Cognitive Neural Network (MCNN) for real –valued neural network. Projection Based Learning with Meta-Cognitive Radial Basis Function Network (PBL-McRBFN) for complex valued neural network. Finally about Meta-Cognitive Fuzzy Inference System (MCFIS) using the Neuro - Fuzzy inference system for learning. The previously said SRAN works on the basis of self – regulatory mechanism in order to reduce the huge loss error and to maximize the class – wise significance. The methods such as MCNN, PBL-McRBFN and MCFIS execute on the human learning strategies such as what – to-learn, when –to-learn and how –to – learn. This review helps to select the learning methods suitable for the data that is to be classified.

Online social networks such as Twitter and Facebook are becoming popular form of social information networks. There are frequently many kinds of relationships in an online social network. Complex Network acting as one kind of big data technologies is often used to analyze users' social activities. By studying the Douban network, which is a representative multi-relationship online social network in China, big data of friendship relationship and book comments similar relationship are crawled through network topology measurement software, from the perspective of topological characteristics of complex network, the basic topologies of the two relationship networks constructed individually by the two relationships are analyzed. Based on these, a multi-relationship online social network based on Multi-subnet Composited Complex Network Model is constructed through loading book comments similar relationship subnet to follower relationship subnet, and accurate understanding of topologies of Douban multi-relationship network is obtained. These findings provide a deep understanding on the evolution of multi-relationship online social network, and can provide guidelines on how to build an efficient multi-relationship online social network evolution model.

With the rapid development of mobile technologies, more and more people are equipped with smartphones. It is possible for scientists to collect and analyze mobile data efficiently. Mobile data contain rich semantic as well as topological information. Rich information can be inferred from these data such as social influence among different nodes in mobile social network. However, it is difficult to estimate the strength of social influence due to the characteristics of inherent dynamic and large scale of mobile social network. In this paper, a Dynamic Influence Graph (DIG) model is proposed which utilizes temporal information in a topological perspective, and an efficient algorithm is proposed based on the DIG model. The proposed algorithm can calculate social influence between any two nodes in a given mobile social network stream segment, and takes edge weights, node connectivity and temporal information into consideration. Experimental results with a real mobile social network dataset show that the proposed approach can infer social influence and achieve a-state-of-the-art accuracy (82-86%) efficiently and automatically.

In the age of Internet, the data transmission and storage got rapid progress, however, data processing and information extraction is still exist many problems to solve. Under the condition of so much data, processing data, get useful information; In cloud computing, big data environment to adopt the method of distributed computing, such a large complex networks, however, requires a simulation environment, for comparison and optimization platform, it can save development costs. Hadoop can evaluate the performance of distributed cloud computing platform, so the Hadoop performance directly affects the evaluation on the performance of the big data cloud computing, which fully show the importance of performance of Hadoop. Algorithm is improved based on Hadoop platform, using the particle swarm optimization algorithm improved the calculation and implementation of the Hadoop platform, so as to improve its ability to execute and compute, the calculation results and analysis show that the proposed scheme is effective.

In WWW, due to the distribution of information resources, how to manage and maintain distributed information resources in different nodes is one of the most important problems in the current research. On the basis of analyzing the scalability, efficiency and effectiveness of data exchange, the paper designed a distributed storage model, discussed the organization of the registry information, put forward a dynamically register and update mechanism, designed a distributed information search algorithm. Experimental results show that the biased flooding search algorithm based on index can not only reduce the message load but also can improve the search hit rate.