Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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Big Data Storage Tools Using NoSQL Databases and Their Applications in Various Domains: A Systematic Review
Get PDFOver the past few years, data has been growing significantly due to the advent of new connected devices, availability of bandwidth, and the emergence of new applications which utilize cloud computing infrastructure in the data centers. This increased amount of data faces many problems in terms of storage, transmission, management, and processing, etc. Therefore, the term big data has gained significant attention from researchers in recent years. The rapidly growing quantity, velocity, and variety of data require more probable and logical tools for its storage. For this purpose, the industry is highly emphasizing the development of more viable tools for the storage of big data. The traditional big data storage tools are unsuccessful in storing an enormous amount of data. Hence, the structural modifications of management mechanisms of conventional storage systems such as SQL databases to NoSQL databases technology are necessary to cope up with drastically increasing requirements of big data storage. The primary objective of this paper is to concentrate exclusively on designing a road map for NoSQL big data storage technologies, evaluate current evidence, research progresses in NoSQL data storage systems and their applications in various domains. We conducted a systematic literature review (SLR) of various studies published in recent years. We propose a framework to classify selected articles on the basis of various factors such as motivations behind big data storage, NoSQL techniques used for storing big data, and significant applications of big data in different domains. Furthermore, we also discuss research issues and define an outline for future research in the big data storage domain for NoSQL databases
AllScale API
Get PDFEffectively implementing scientific algorithms in distributed memory parallel applications is a difficult task for domain scientists, as evident by the large number of domain-specific languages and libraries available today attempting to facilitate the process. However, they usually provide a closed set of parallel patterns and are not open for extension without vast modifications to the underlying system. In this work, we present the AllScale API, a programming interface for developing distributed memory parallel applications with the ease of shared memory programming models. The AllScale API is closed for a modification but open for an extension, allowing new user-defined parallel patterns and data structures to be implemented based on existing core primitives and therefore fully supported in the AllScale framework. Focusing on high-level functionality directly offered to application developers, we present the design advantages of such an API design, detail some of its specifications and evaluate it using three real-world use cases. Our results show that AllScale decreases the complexity of implementing scientific applications for distributed memory while attaining comparable or higher performance compared to MPI reference implementations
Controlled Experiment for Assessing the Contribution of Ontology Based Software Redocumentation Approach to Support Program Understanding
Get PDFRedocumentation is an approach that is used to recover knowledge from raw software artifacts by using alternative presentations. Several legacy systems have been developed based on event-driven programming which require redocumentation. However, these existing repository and query techniques emphasize only on lexical and syntactical based queries which come with limitations in providing the semantic relationship for program understanding. We are using ontology based approach that uses both ontology reasoning and querying techniques to generate software documentation from the knowledge repository. We present a controlled experiment for the empirical evaluation on the proposed ontology based approach and implemented in a tool called Ontology Based Software Redocumentation (OBSR). In this experiment, two existing tools namely Universal Report (UR) and Microsoft Visual Studio specifically for Visual Basic (VB) programming environment have been selected to be compared with the OBSR tool. The goal is to provide experimental evidence of the viability of our approach in the context of program understanding using HTML based semantic software documentation. The experiment shows that the software maintainers are able to understand and provide significant improvement in program understanding to accomplish the maintenance task easily. We describe in detail the experiment performed, discuss its results and reflect the lesson learned from the experiment
Ciphertext-Policy Attribute Based Encryption with Selectively-Hidden Access Policy
Get PDFIn conventional Ciphertext-Policy Attribute-Based Encryption (CP-ABE), the access policy appears in plaintext form that might reveal confidential user information and violate user privacy. CP-ABE with hidden access policies hides all attributes, but the computational burden increases due to the attribute hiding. In this paper, we present a Linear Secret Sharing Scheme (LSSS) access structure CP-ABE scheme that hides only sensitive attributes, rather than all attributes, in the access policy. We also provide an attribute selection method to choose these sensitive attributes and use an Attribute Bloom Filter (ABF) to hide them. Compared with the existing major CP-ABE schemes with hidden access policies, our proposed scheme is flexible in selecting attributes to hide. This scheme enhances the efficiency of policy hiding while still protecting policy privacy. Test results show that our approach is reasonable and feasible
Hierarchical Text Classification Using CNNs with Local Approaches
Get PDFIn this paper, we discuss the application of convolutional neural networks (CNNs) for hierarchical text classification using local top-down approaches. We present experimental results implementing a local classification per node approach, a local classification per parent node approach, and a local classification per level approach. A 20Newsgroup hierarchical training dataset with more than 20 categories and three hierarchical levels was used to train the models. The experiments involved several variations of hyperparameters settings such as batch size, embedding size, and number of available examples from the training dataset, including two variation of CNN model text embedding such as static (stat) and random (rand). The results demonstrated that our proposed use of CNNs outperformed flat CNN baseline model and both the flat and hierarchical support vector machine (SVM) and logistic regression (LR) baseline models. In particular, hierarchical text classification with CNN-stat models using local per parent node and local per level approaches achieved compelling results and outperformed the former and latter state-of-the-art models. However, using CNN with local per node approach for hierarchical text classification underperformed and achieved worse results. Furthermore, we performed a detailed comparison between the proposed hierarchical local approaches with CNNs. The results indicated that the hierarchical local classification per level approach using the CNN model with static text embedding achieved the best results, surpassing the flat SVM and LR baseline models by 7 % and 13 %, surpassing the flat CNN baseline by 5 %, and surpassing the h-SVM and h-LR models by 5 % and 10 %, respectively
Effect of Term Weighting on Keyword Extraction in Hierarchical Category Structure
Get PDFWhile there have been several studies related to the effect of term weighting on classification accuracy, relatively few works have been conducted on how term weighting affects the quality of keywords extracted for characterizing a document or a category (i.e., document collection). Moreover, many tasks require more complicated category structure, such as hierarchical and network category structure, rather than a flat category structure. This paper presents a qualitative and quantitative study on how term weighting affects keyword extraction in the hierarchical category structure, in comparison to the flat category structure. A hierarchical structure triggers special characteristic in assigning a set of keywords or tags to represent a document or a document collection, with support of statistics in a hierarchy, including category itself, its parent category, its child categories, and sibling categories. An enhancement of term weighting is proposed particularly in the form of a series of modified TFIDF's, for improving keyword extraction. A text collection of public-hearing opinions is used to evaluate variant TFs and IDFs to identify which types of information in hierarchical category structure are useful. By experiments, we found that the most effective IDF family, namely TF-IDFr, is identity>sibling>child>parent in order. The TF-IDFr outperforms the vanilla version of TFIDF with a centroid-based classifier
Explanation of Siamese Neural Networks for Weakly Supervised Learning
Get PDFA new method for explaining the Siamese neural network (SNN) as a black-box model for weakly supervised learning is proposed under condition that the output of every subnetwork of the SNN is a vector which is accessible. The main problem of the explanation is that the perturbation technique cannot be used directly for input instances because only their semantic similarity or dissimilarity is known. Moreover, there is no an "inverse" map between the SNN output vector and the corresponding input instance. Therefore, a special autoencoder is proposed, which takes into account the proximity of its hidden representation and the SNN outputs. Its pre-trained decoder part as well as the encoder are used to reconstruct original instances from the SNN perturbed output vectors. The important features of the explained instances are determined by averaging the corresponding changes of the reconstructed instances. Numerical experiments with synthetic data and with the well-known dataset MNIST illustrate the proposed method
PROCESS Data Infrastructure and Data Services
Get PDFDue to energy limitation and high operational costs, it is likely that exascale computing will not be achieved by one or two datacentres but will require many more. A simple calculation, which aggregates the computation power of the 2017 Top500 supercomputers, can only reach 418 petaflops. Companies like Rescale, which claims 1.4 exaflops of peak computing power, describes its infrastructure as composed of 8 million servers spread across 30 datacentres. Any proposed solution to address exascale computing challenges has to take into consideration these facts and by design should aim to support the use of geographically distributed and likely independent datacentres. It should also consider, whenever possible, the co-allocation of the storage with the computation as it would take 3 years to transfer 1 exabyte on a dedicated 100 Gb Ethernet connection. This means we have to be smart about managing data more and more geographically dispersed and spread across different administrative domains. As the natural settings of the PROCESS project is to operate within the European Research Infrastructure and serve the European research communities facing exascale challenges, it is important that PROCESS architecture and solutions are well positioned within the European computing and data management landscape namely PRACE, EGI, and EUDAT. In this paper we propose a scalable and programmable data infrastructure that is easy to deploy and can be tuned to support various data-intensive scientific applications
Effective Feature Extraction Method for SVM-Based Profiled Attacks
Get PDFNowadays, one of the most powerful side channel attacks (SCA) is profiled attack. Machine learning algorithms, for example support vector machine, are currently used for improving the effectiveness of the attack. One issue when using SVM-based profiled attack is extracting points of interest, or features from power traces. So far, studies in SCA domain have selected the points of interest (POIs) from the raw power trace for the classifiers. Our work proposes a novel method for finding POIs that based on the combining variational mode decomposition (VMD) and Gram-Schmidt orthogonalization (GSO). That is, VMD is used to decompose the power traces into sub-signals (modes) of different frequencies and POIs selection process based on GSO is conducted on these sub-signals. As a result, the selected POIs are used for SVM classifier to conduct profiled attack. This attack method outperforms other profiled attacks in the same attack scenario. Experiments were performed on a trace data set collected from the Atmega8515 smart card run on the side channel evaluation board Sakura-G/W and the data set of DPA contest v4 to verify the effectiveness of our method in reducing number of power traces for the attacks, especially with noisy power traces
BalticLSC: Low-Code Software Development Platform for Large Scale Computations
Get PDFIn modern times, innovation often requires performing complex computations in a short amount of time. However, for many small organisations and freelance innovators, large-scale computations remain beyond reach because of the small accessibility of computation resources and the lack of knowledge required to use them efficiently. The BalticLSC Platform is a software development and computing environment created to address this issue. This paper presents the associated software development process. The platform users can perform advanced computations using ready applications or develop new applications quickly from available components. This can be done using a visual notation called the Computation Application Language (CAL). CAL programs are developed in a dedicated online editor, through selecting and connecting reusable computation modules. If a required module is missing, it can be quickly created by encapsulating code inside a standardised container. The platform's ultimate goal is to relieve the developers from the need to understand the complexity of the distributed parallel computation environment. The platform was implemented in the form of an online software development portal. Validation of the platform consisted in the development of applications and modules by students not experienced in programming. The results of this validation acknowledge the required platform's characteristics