Earticle

Event coreference resolution is an important step for business intelligence. In this paper, we present a method for event coreference resolution based on heterogeneous information network. We adopt holistic, hierarchical clustering algorithm to solve this problem. In terms of similarity measure, the proposed method utilizes the relationship between entities, events, documents and data source, calculates event comprehensive similarity through the event properties similarity. Experiment on business event sets, characters event sets and product event sets, the experiment results show that the algorithm of this paper can effectively complete event coreference resolution, and has good recall and precision.

There has been a high increase in air traffic for the past ten years and to attract more customers many in-flight facilities are introduced by the carriers. These include Video-On-Demand, use of Personal Digital Assistants (PDA) etc. To provide these facilities the VoIP technology is used more often. But maintaining VoIP infrastructure along with the Aircraft Data Network equipment’s will be more cumbersome for the carriers, along with the added infrastructure cost. Thus an improvised architecture has been proposed which helps carriers provide these facilities to the customers in a cost effective and reliable manner. Most of the aircraft communications to the ground rely on satellite for providing these services to the customers. The proposed method uses satellite communication along with cloud based VoIP to provide an efficient quality of service to the passengers. The ground stations used for satellite communication would be connected to a high speed Storage Area Network, to which the cloud network on which the VoIP application is running is also connected. So that whenever a passenger on the flight sends a message/data to the VoIP application first it would be send to the ground stations via the satellite which is then accessed through the SAN server, thus in turn providing faster, reliable and efficient communication to the customers.

In the past decades, plenty scheduling policies have been proposed for improving the execution performance of workflow applications. However, few of them have addressed the issue of energy conservation. In this paper, a novel scheduling metric, namely Minimal Energy Consumption Path, is introduced to reducing the energy consumption when scheduling large-scale workflows. The proposed scheduling metric can be incorporated into current schedulers so as to enhance their capability of energy conservation as well as maintain their scheduling performance. A lot of experiments are performed by using different scheduler and experimental settings to evaluate the proposed scheduling metric and the results indicate it is effective for reducing the storage-related energy consumption, especially when the target workflows are data-intensive.

Energy consumption is one of the most challenging constraintsof the design and implementation of the sensor network. Underwater sensor networking is the technology that enables the applications like environment monitoring, underwater exploration, seismic monitoring and other surveillance applications. In underwater sensor network,a sensor node senses the data and transmits it to the sink. Many routing algorithms have been proposed in order to make the network phase of UWSNs more efficient.In this report, we present a review and comparison of various data collection algorithms and clustering schemes, proposed recently in order to execute the demands of the ongoing researches. The main goal of data aggregation technique is to accumulate data in an energy efficient manner for a long-term network monitoring. The main purpose of this study is to present algorithms addressing issues like deployment and localization in UWSNs under different conditions.

MapReduce, a large-scale data processing paradigm, is gaining popularity. However, like other distributed computing frameworks, MapReduce suffers from the integrity assurance vulnerability: malicious workers in the MapReduce cluster could tamper with its computation result and thereby render the overall computation result inaccurate. Existing solutions are effective in defeating the malicious behavior of non-collusive workers, but are less effective in detecting collusive workers. In this paper, we propose the Verification-based Integrity Assurance Framework (VIAF). By using task replication and probabilistic result verification, VIAF can detect both non-collusive and collusive workers, even if the malicious workers dominate the environment. We have implemented VIAF on Hadoop, an open source MapReduce implementation. Our theoretical analysis and experimental result show that VIAF can achieve high job accuracy while imposing moderate performance overhead.

Memory ballooning is dynamic memory management technique for virtual machines (VMs). Ballooning is a part of memory reclamation technique operations used by a hypervisor to allow the physical host system to retrieve unused memory from certain guest virtual machines (VMs) and share it with others. Memory ballooning allows the total amount of RAM required by guest VMs to exceed the amount of physical RAM available on the host. Memory overcommitment enables a higher consolidation ratio in a hypervisor. Using memory overcommitment, users can consolidate VMs on a physical machine such that physical resources are utilized in an optimal manner while delivering good performance. Hence memory reclamation is an integral component of memory overcommitment. In this paper, we address that the basic cause of memory that ballooning is memory overcommitment from using memory-intensive virtual machines. We compared to others reclamation technique and identify Cost Associate with Memory Ballooning in state of Memory Overcommitment. The objective of this paper is to analyse memory ballooning technique for dynamic memory management of VMs. For this analysis, VMware based virtualization software, e.g., ESXi Server, vCenter Server, vSphere Client are installed and configured on the Centre for Innovation and Technology (CIT) Lab, DIU; for monitor and analyze VM performance for memory ballooning technique. The performance of memory ballooning technique is evaluated with two different test cases. The purpose is to help users understand, how this technique impact the performance. Finally, we presents the throughput of heavy workload with different memory limits when using ballooning or swapping; and analyse VM performance issue for this technique.

In recent years, cloud computing has drawn more and more attention. Increasingly amount of systems and applications have been constructed in cloud environments, yet few researches of rule engine has been done. Rule engine technologies have been widely used in the development of enterprise information systems. These rule-based systems may encounter the problem of low performance, when a large amount of fact data are matched with these rules. Deploying rule engines in cloud environments can increase the capability and efficiency of these systems. In this paper, we propose an approach to implement rule engine based on a message-passing concurrency model in cloud computing. The approach can be extended conveniently and it can deal with extensive rules and facts efficiently. To improve the performance of the rule engine, an algorithm of allocation is proposed. A resource cost model is explored to make high efficient use of resources in cloud. In addition, we implement the rule engine system RUNES II in cloud platform and conduct experiments to show its performance.

This manuscript introduces a novel design concept for advanced mobile terminals with radio network aggregation capability and enhanced Quality of Service (QoS) provisioning for various multimedia services in heterogeneous wireless and mobile networks. We establish a new module that provides the best QoS and lowest cost for any given multimedia service (voice, video and audio) by using simultaneously all available wireless and mobile networks for a given traffic flow. The performance of our proposal is evaluated using simulations with multimode mobile stations carrying multimedia traffic in heterogeneous environment with coexistence of multiple Radio Access Technologies, such as 3G, 4G as well as future 5G radio access networks. The analysis of the proposed framework for radio networks aggregation in advanced mobile terminals has shown overall better performances regarding the achievable throughput and multimedia access probability in heterogeneous wireless and mobile environment.

Enterprise informatization is a large system including: input, output, feedback, operation inside, executive action and incentive measures. Inner-enterprise can be seemed as an artificially social organization unit composing of various agents in a rigid formation. This paper mainly proposes an enterprise informatization associated with rigid formation theory of multi-agent. The connection between agents and enterprise factors is represented. The relationship between enterprise factors is described by potential function theory: the attractive force presents that all factors have the same opinion, and the repulsive force presents that all factors have the different opinion needed to be solved. Finally, the simulation proves that the model mentioned in this paper is effective.

Cloud Computing Environment, the data presides over a set of networked resources and these data centers may be located in any part of the world and access of the data provided through Internet. Cloud computing facilitates computing assets on demand by the use of a service provider. In the Current Scenario, Security and privacy challenges are facing in this cloud environment. We implement our scheme and show that it is both efficient and flexible in dealing with access control for outsourced data in cloud computing with comprehensive experiments using the technique Hierarchical Attribute Based Encryption to protect data of the users and analyze its performance.

With the expansion of mobile Internet, location-based services have become a hot spot in Internet industry. To improve the accuracy and efficiency of the location-based services discovery, researchers in web services recommendation area are still busy looking for method. In this paper, it proposes a LCAMSP model (Location Context Awareness Mobile Service Prediction, LCAMSP) under mobile Internet environment, aims to meet the exact personal requirements of users’ current location and preference. Then, the similar users’ grouping is also a important thread to predict the continuous movement for mobile users. Because each user has own preference, a dynamic calculation function for the weight of each attribute is discussed in this paper. Finally, taking the hotel reservation service as an example, a verification algorithm will be applied to measure the performance of LCAMSP method. With the premise of increasing limited time, the accuracy of LCAMSP algorithm is significantly improved under the mobile service environment.

The operational capacity of an airport must be dimensioned under a long-term strategic view as its productivity is determined by available infrastructure. To predict the airport capacity and satisfy the domestic airport property, the operation procedure of airport is analyzed from the point of Multi-agent system. The proposed agent based architecture is a microscopic model of aircrafts' movement; each aircraft is represented as an agent capable of getting its routing results from instructing agent, and makes its own deconflicition decisions on the basis of a part of the observable scene local to that aircraft. An example of airport capacity evaluation is given, and the result is verified through comparison with SIMMOD. Finally, further research is applied to analysis the reason for delay.

Massive data storage is one of the great challenges for cloud computing service, and reliable storage of sensitive data directly affects quality of storage service. In this paper, based on analysis of data storage process in cloud environment, the cost of massive data storage is considered to be comprised of data storage price, data migration and communication; and the storage reliability consists of data transmission reliability and hardware dependability. A multi-objective optimization model for reliable massive storage is proposed, in which storage cost and reliability are the objectives. Then, a genetic algorithm for solving the model is designed. Finally, experimental results indicate that the proposed model is positive and effective.

In recent years, distributed computing technology has been one of the cutting edge technologies for its low power and cost, which makes numerous IT organizations extend their hands in order to improve their financial ability. Because of these new features, grid computing, the original task scheduling mechanism, can’t work effectively in distributed computing environments, hence, we need a new task scheduling method to solve the problems. With considering the complex characters of the task in different distributed computing applications, firstly, we construct a more comprehensive task scheduling model, which has three sub objective functions. Secondly, we present an improved genetic algorithm to solve the multi- objective NP-hard problem. Finally, we implement some simulation experiments, and the evaluation results show us that the proposed model and improved GA are efficient and effective. The first part is the research status and related problems. The second part is the establishment of system architecture and task scheduling model. The last part is the experimental analysis and conclusion.

Cloud computing is a model to provide pool of services on demand which is shared among the consumers and metered. Cloud deployment should support efficient mechanism to publish its services in a right method such that the end users identify them. Services has to be discovered according to user’s requirement dynamically. The proposed work discusses about introducing semantics in the cloud services description, such that it projects itself apart from other providers and capably handle the commercial demand for their services. The semantics are introduced at the service catalog level and the CSP publish their list of services in the form of RDF, whose semantics are defined in a provider specific ontology. The system was evaluated with keyword & frequency matching discovery and proved to be efficient with semantic discovery over RDF data.

Data mining is an important tool in meteorological problems solved. Cluster analysis techniques in data mining play an important role in the study of meteorological applications. The research progress of the clustering algorithms in meteorology in recent years is summarized in this paper. First, we give a brief introduction of the principles and characteristics of the clustering algorithms that are commonly used in meteorology. On the other hand, the applications of clustering algorithms in meteorology are analyzed, and the relationship between the various clustering algorithms and meteorological applications are summarized. Then we interpret the relationship from the perspectives of algorithms’ characteristics and practical applications. Finally, some main research issues and directions of the clustering algorithms in meteorological applications are pointed out.

A Mobile Ad hoc Network (MANET) can be defined as a network of mobile nodes that communicate over the wireless radio communication channel. It is also defined as a network without any underlying infrastructure and offers unrestricted mobility. Due to their open nature and lack of infrastructure, security for MANETs has become an intricate problem. To transmit data over such a network, generally any routing protocol that enables dynamic, self-starting multi hop routing between mobile nodes is used. However these routing protocols are vulnerable to various kinds of attacks. The conventional security mechanisms of protecting a network are not sufficient for these networks. Hence a second level of defense to detect and respond to the security problem called an Intrusion Detection System (IDS) is required. An IDS based on anomaly based intrusion detection that works by checking the behavior of the nodes was proposed to overcome some of the attacks like blackhole, grayhole and flooding attacks. Generally the malicious nodes demonstrate a different behavioral pattern of all the other normal nodes. So the specified approach where a Data Transmission Quality (DTQ) function is used to determine the behavior of the nodes as malicious or legitimate is used. The DTQ function is defined in such a way that it will be close to a constant or keep changing smoothly for genuine nodes and will keep on diminishing for malicious nodes. The proposed method was implemented using AODV as the routing protocol for transmitting data. The evaluation results show that the performance of the AODV protocol under attacks has improved significantly by using the proposed approach.

According to the traditional stabilized biconjugate gradient algorithm (BiCGSTAB) deficiency in data locality, this paper proposed a QTBiCGSTAB algorithm whose core idea is that recursively divides sparse matrix with quarter tree into sub-matrix and reorders them, to improve the hit ratio of cache and enhance the algorithm’s efficiency. And the idea is good for algorithm being parallized, that is proved by the numerical experiments later. It mainly shows, firstly, QTBiCGSTAB algorithm is more efficiency than BiCGSTAB, and the speedup would reach 1:330. The target division length would be influnced on the algorithm’s performance; Secondly, for large linear system, parallized QTBiCGSTAB is more efficiency than serial’s.

To realize the equal sharing and full use of manufacturing resource, aiming at the convergence characteristics and dynamic changing behaviors of the distributed and stochastic manufacturing resources, according to the thought of peer-to-peer (P2P) and the Chord protocol, a distributed manufacturing resource sharing system model is proposed, making logic sub rings by the type of resources and the logic main rings by the super nodes. The relay of the resource rings and the convergence of different types of resources for application are completed by super nodes. The description template of the manufacturing resources and the Meta data model relating to the search keyword sets from the template are designed. The indexing process of the resources based on Meta data model and the hierarchical trust mechanism of the sharing system are elaborated; finally, an instance of the sharing system prototype is given.