Earticle

An overwhelming amount of data arriving from disparate sources can be a bottleneck in realization of any service. This data, if properly processed and annotated, can be a useful source of information. This work builds an Area Profile Ontology (APO) and imports other related ontology to annotate rich data arriving from multiple sensor streams like cameras. The annotation process provides an impetus to the improvement of knowledge over time. APO provides the main concepts and properties to model an area which can span even a city. We define Jena rules to infer intelligent information about the position of an object of our interest. We illustrate ontology emergence by drawing upon a case study to show an actual application.

Data mining is the process of extracting useful and yet unknown information such as patterns or associations hidden in stored data. Among various existing techniques applied to search for interesting patterns, decision tree is one of the most popular tools used for data mining. Most data mining techniques are data-driven, however, data repositories of interest in data mining applications can be very large and noisy. Noise is a random error in data. Noise in a data set can happen in different forms: misclassification or wrong labeled instances, erroneous or distorted attribute values, contradictory or duplicate instances having different labels. All kinds of noise can more or less affect the learning performance. The most serious effect of noise is that it can confuse the learning algorithms to produce complex and distorted results. The long and complex results are due to the attempt to fit every training data instance, including noisy ones, into the concept descriptions. This is a major cause of overfitting problem. Most learning algorithms are designed with the awareness of overfitting problem due to noisy data. Prepruning and postprocessing are two major techniques applied to avoid growing a decision tree too deep down to cover the noisy training data. These techniques are tightly coupled to the tree induction phase. We, on the contrary, design a loosely coupled approach to deal with noisy data. Our noise-handling feature is in a separate phase from the tree induction. Both corrupted and uncorrupted data are clustered and heuristically selected prior to the application of tree induction engine. We observe from our experimental study that tree models produced from our approach are as accurate as the models generated by conventional decision tree induction approach. Moreover, upon highly corrupted data our approach shows a better performance than the conventional approach.

Phase noise is produced in local oscillator or PLL(phase locked loop), which affects seriously BER performance of the OFDM (orthogonal frequency division multiplexing) system. ICI (inter-sub-carrier-interference) caused by the phase noise is a serious problem. In this paper, two modified ICI self-cancellation methods are proposed and compared for the performance evaluation. In the original OFDM system, the proposed symmetric data-conjugate method has the best ICI minimization capability, which becomes gradually degraded according to the order of symmetric data-conjugate method, data-conjugate method, symmetric data-conversion method and data-conversion method. When phase noise dominates in the OFDM system, symmetric data-conjugate method can result in the best BER performance, when HPA nonlinearity cannot be neglected, data-conjugate method achieves the best BER performance.

An efficient functionality of any distributed database system “DDBS” is highly dependent on its proper design in terms of the adopted fragmentation and allocation methods. However, an optimal fragmentation and allocation design techniques could be very complex and requires good experiences and knowledge to reach. Fragmentation of a large, global databases are performed by dividing the database relations horizontally, vertically or as a combination of both. In order to enable distributed database systems to work efficiently, these fragments have to be allocated across the available sites in such a way that reduces communication cost i.e. to minimize the total volume of data transmitted during queries execution over sites. This paper presents a new data re-allocation model for replicated and non-replicated constrained DDBSs by bringing a change to data access pattern. This approach assumes that the distribution of fragments over network sites was initially performed according to a properly forecasted set of query frequency values that could be employed over sites. Our model takes sites constraints into account in the re-allocation phase. It proposes an efficient plan to re-allocate data fragments across sites based on communication and update cost values for each fragment individually. The re-allocation process will be performed by selecting the maximal update cost value for each fragment and making the re-allocation accordingly. Experimental results confirmed that the proposed technique will effectively contribute in solving fragments re-allocation problem in a dynamic distributed relational databases environment.

Microarray gene expression techniques and tools have become of a substantial importance and widely used to analyze the protein-protein interaction (PPI) and gene regulation network (GRN) research in recent years since it can capture the expressions of thousands of genes in a single experiment. Such dataset poses a great challenge for finding association rules in a faster way because of the presence of large number of columns but a small number of rows. Therefore, to meet the challenge of high volume of gene expression and the complexity of microarray data, various data mining methods and applications have been proposed for analyzing gene expressions. However, it is not trivial to extract biologically meaningful information from the huge amount of gene expression data in understanding of gene regulation networks and cellular state, because most cellular processes are regulated by changes in gene expression. Association rule mining techniques are helpful to find relationship between genes, but most of the developed association rule mining algorithms are based on main memory and single processor based techniques which are not capable of handling ever increasing large data and producing result in a faster way. In this paper, we proposed a MapReduce framework for mining association rules from a huge microarray gene expression dataset on Hadoop; which not only overcomes of the main memory bottleneck but also highly scalable in terms of increasing data size. When we apply this new method to the mice lungs and spinal cord microarray compendium data, it identifies a majority of known regulons as well as novel potential target genes of numerous key transcription factors. Extensive experimental results show that our proposed approach is efficient for mining high confident association rules from large microarray gene expression datasets in terms of time and scalability.

Rapidly increasing growth and demand in CNS/ATM, the advanced scheme for air traffic management, ADS-B system which is based on digital communication is being implemented in the field of surveillance. ADS-B concept appears to be an economically viable solution to obtain accurate aircraft position information. ADS-B system has better performance than the traditional radar and ADS-B system replace the radar in the near future. However, as the position information of the ADS-B is obtained from the GPS, the accuracy of the ADS-B lies on GPS. Therefore, the ADS-B information includes GPS errors. In this paper, we applied the unscented kalman filter to the ADS-B information to improve the accuracy of the ADS-B information. Comparisons with the original ADS-B information and the ADS-B information with the unscented kalman filter show that the ADS-B information with the unscented kalman filter provides the better.

Traditional database security mechanisms focus on either protection or prevention. However, these mechanisms have not any strategy in the presence of successful attacks. To solve this problem, the Intrusion Tolerant Database System (ITDB) was introduced. ITDB uses the new generation of database security mechanisms to guarantee specified levels of data availability, integrity and confidentiality in the presence of successful attacks. These mechanisms include Attack Isolation and Multiphase Damage Confinement. In this paper, we will present a practical model to utilize the combination of intrusion tolerance techniques for managing the ITDB architecture. Using this practical model, we will be able to secure the system’s required integrity and availability levels considering the changes in the environment. We will also introduce an intelligent method for determining the significance degrees of data objects in the optimized attack isolation technique.

This paper presents the overview of the Android platform towards software development for mobile and non-mobile applications. Android platform includes the popular open source SQLite database which has been used with great success as on-disk file format that allows the developer to handle data in a simple way, but also have the use of database features (such as undo, redo, etc.).