Vol. 40 · No. 3

Formal Concept Analysis (FCA) is a mathematical framework which can also support critical activities for the development of the Semantic Web. One of them is represented by Similarity Reasoning, i.e., the identification of different concepts that are semantically close, that allows users to retrieve information on the Web more efficiently. In order to model uncertainty information, in this paper FCA with many-valued contexts is addressed, where attribute values are intervals, which is referred to as FCA with Interordinal scaling (IFCA). In particular, a method for evaluating concept similarity in IFCA is proposed, which is a problem that has not been adequately investigated, although the increasing interest in the literature in this topic.

Over 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.

With the ever growing number of edge devices, the idea of resource sharing systems is becoming more appealing. Multiplayer games are a growing area of interest due to the scalability issues of current client-server architectures. A paradigm shift from centralized to decentralized architectures that would allow greater scalability has gained a lot of interest within the industry and academic community. Research on peer to peer network protocols for multiplayer games was mainly focused on cheat detection. Previously proposed solutions address the cheat detection issues on a protocol level but do not provide a holistic solution for the architecture. Additionally, existing solutions introduce some level of centralization, which inherently introduces single point of failures. We propose a blockchain-based, completely decentralized architecture for edge devices with no single point of failure. Our solution relies on an innovative consensus mechanism based on verifiable delay functions that additionally allows the network to derive verifiable randomness. We present simulation results that show the assignment of players and referees to instances is pseudo-random, which inherently prevents collusion-based cheats and vulnerabilities.

Low Level Virtual Machine (LLVM) is a widely adopted open source compiler providing numerous optimization opportunities. The discovery of the best optimization sequence in this large space is done via iterative compilation, which incurs substantial overheads, especially for big data applications operating on high volume and variety datasets. The large search space is mostly comprised of identical codes generated via different optimizations. However, no mechanism is implemented inside the LLVM compiler to suppress the redundant testings. In this regard, this paper proposes REDUCER for eliminating the identical code executions by performing Intermediate Representation (IR) level comparisons. REDUCER has been tested using the well-accepted MiCOMP technique in LLVM 3.8 and 9.0 compiler, with embedded (cBench) and big data workloads. In comparison to MiCOMP 19.5 k experiments, REDUCER lowers the experiment count up to 327, i.e. 98

Software testing is one of the very important Quality Assurance (QA) components. A lot of researchers deal with the testing process in terms of tester motivation and how tests should or should not be written. However, it is not known from the recommendations how the tests are written in real projects. In this paper, the following was investigated: (i) the denotation of the word ``test'' in different natural languages; (ii) whether the number of occurrences of the word ``test'' correlates with the number of test cases; and (iii) what testing frameworks are mostly used. The analysis was performed on 38 GitHub open source repositories thoroughly selected from the set of 4.3 M GitHub projects. We analyzed 20 340 test cases in 803 classes manually and 170 k classes using an automated approach. The results show that: (i) there exists a weak correlation (r = 0.655) between the number of occurrences of the word ``test'' and the number of test cases in a class; (ii) the proposed algorithm using static file analysis correctly detected 97

Today, mobile advertising is considered as the most effective medium to convey promotional messages to customers because of the excessive usage of mobile phones and tablets all around the world. However, this ecosystem has severely been affected by fraudulent activities due to a large sum of money circulated in the advertising industry. The term ad fraud is referred to as any kind of fraudulent activities that are executed by fraudulent users either a human or an automated script. The combat between researchers and fraudulent users never ends because more smarter strategies are being used by the fraudsters to bypass the significant number of detection and prevention solutions. The Real Time Mobile Ad Investigator-RTMAI is proposed as a software solution to address this problem where a novel supervised learning algorithm based on the hidden Markov model along with a rule engine have been proposed to classify fraudulent impressions in real time. Furthermore, RTMAI proposed a solution to address the class imbalance problem which is generic to most of the classification datasets. The experimental results show the significance of the proposed approach to classify the fraud or non-fraud clicks/events, impressions and even user sessions more confidently in real time.

Due to increase in the availability of numerous languages in the Web, cross language information retrieval is one of the happening issues in the field of natural language processing and information retrieval. Nowadays, people are habituated to combine two or more language words during oral or written discourse. Speakers have also employed intermixing of different languages and scripts in digital media while querying, blogging and on social media platforms. The way of representing two different language words of an utterance in their native scripts is known as mixed scripting. In the present work, we attempted to translate mixed script queries of Kannada and English languages into monolingual queries. We proposed three approaches for translation by constructing bilingual dictionary, word embeddings and Google translate. The proposed method outperforms the conventional dictionary based approach, when word embeddings were combined with the translations learnt from Google Translate and Dictionary.

Solving the deadlocks avoidance problem in Resource Allocation Systems (RAS) in Discrete-Event Systems (DES) is a rife problem, especially in Flexible Manufacturing Systems (FMS), alias Automated Manufacturing Systems (AMS). Petri Nets (PN) are an effectual tool often used at this procedure. In principle, there are two basic approaches how to deal with deadlocks in RAS based on PN. They are listed and illustrated here. First of the approaches is realized by means of the supervisor based on P-invariants of PN, while the second one is realized by means of the supervisor based on PN siphons. While the first approach needs to know the reachability graph/tree (RG/RT) expressing the causality of the development of the PN model of RAS, in order to find (after its thorough analysis) the deadlocks, the second approach needs the thorough analysis of the PN model structure by means of finding siphons and traps. Next, both approaches will be applied on the same PN model of RAS and the effectiveness of the achievement of their results will be compared and evaluated. Several simple illustrative examples will be introduced. For the in-depth analysis of the problem of deadlock avoiding, next Part 2 of this paper is prepared, where the newest research will be introduced and illustrated on more complicated examples. If necessary (because of the limited length of particular papers), also the third part – Part 3, will be prepared.

Over the past few decades, clone detection and evolution have become a major area of study in software engineering. Clone detection experiments present several challenges to researchers such as accurate data collection, selecting proper code detection algorithms, and understanding clone evolution phenomena. This paper attempts to facilitate clone detection and evolution research by providing a structured and systematic mechanism to conduct experiments. Clone detection experiments usually consist of several tasks such as fetching data from a version control system, performing necessary pre-processing activities, and feeding the data to a clone detection algorithm. Therefore, a particular clone detection experiment can interpret as a meaningful combination of such tasks into a scientific workflow. In this work, the concrete tasks in a code clone detection workflow are referred to as Building Blocks. This paper presents a useful collection of Building Blocks identified based on a systematic literature review, and a conceptual framework of an experimental testbed to facilitate clone detection experiments. The reusability of the Building Blocks was validated using four case studies selected from the literature. The validation results confirm the reusability and the expressiveness of the Building Blocks in new ventures. Besides, the proposed experimental testbed is proven beneficial in conducting and replicating clone detection experiments.