iosr-JCE   Volume-22 ~ Issue-1 ~Jan - Feb 2020

Abstract: Big Data is the leading topic in the current e youth, the data they generate while communicating with each other. E youth is not the only reason of generating Big Data. The rapid growth of Internet uses, Sensors, IOT, and computing led to the extensive growth of data in various industries and field. This paper provides a brief about Big Data. It also covers the thought of various and characteristics described by various author about Big Data. The technology used in Big data. This paper also describes about various challenges associated to Big Data..

Keywords: Big Data, NOSQL, 5 Vs, Pig, HBase, Cloudera, RC File, Hadoop etc.

[1]. V. Mayer-Schonberger, K. Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think, Houghton Mifflin Harcourt, 2013.
[2]. R. Thomson, C. Lebiere, S. Bennati, Human, model and machine: a complemen-tary approach to big data, in: Proceedings of the 2014 Workshop on HumanCentered Big Data Research, HCBDR '14, 2014.
[3]. A. Cuzzocrea, Privacy and security of big data: current challenges and future research perspectives, in: Proceedings of the First International Workshop onPrivacy and Security of Big Data, PSBD '14, 2014.
[4]. K. Krishnan, Data warehousing in the age of big data, in: The Morgan Kaufmann Series on Business Intelligence, Elsevier Science, 2013.
[5]. A. Reeve, Managing Data in Motion: Data Integration Best Practice Techniques and Technologies, Morgan Kaufmann, 2013

Abstract: Typically the software testing process is aiming Bug Detection, Bug Prevention, User Satisfaction, Software quality and reliability, Recommendations. However, just discovering bug is not enough unless the causes are not identified. Any software can be considered as a complex dynamic system. Accordingly, the testing process is described as a complex multy parameter task. Based on software testing results, paper proposes representation of software qualityas so-called "quality concepts" that form a kind of a library.The "quality concepts" are connected to appropriate "error concepts" that serve a specific purpose of "poor quality" (error or inaccuracy in software provision). The concepts of 'quality' and 'error' are developed using the analytical heuristics method. Subsequently, these concepts will create a library of "defective software templates"..

Keywords: Detect errors, Analytical Heuristics Method, Concepts

[1]. P.K. Chaurasia, R.A. Khan. Classification of Software Requirement Errors: A Critical Review. International Journal of Computer Applications (0975 – 8887) Volume 132 – No.7, December 2015. Pp. 9-14
[2]. Chavchanidze V. V. Analiticheskiye evristiki iskussnvennogo intellekta pri formirovanii ponyatiy, opoznanii obrazov i klassifikatsii obyektov. VINITI. № 2080-70 Dep. (Analytical Heuristics of the Artificial Intellect in the Formation of Notions, Identification of Images and Classification of Objects, VINITI, #2080-70 Dep.
[3]. Chavchanidze V. V. Analiticheskoye Resheniye Zadachi Formirovaniya Ponyatyi i Raspoznavaniya Obrazov (Analytical Resolution of the Problem of Identification of Notions and Recognition of Images). Proceedings of the Academy of Sciences of the Georgian SSR, vol.63, No.1, 1971.
[4]. A.J. Ko, B.A. Myers. A framework and methodology for studying the causes of software errors in programming systems. Journal of Visual Languages and Computing 16 (2005) 41–84
[5]. P. Tr ̈oger, L. Feinbube, M. Werner, What activates a bug? a refinement of the laprie terminology model, in: Software Reliability Engineering (ISSRE), 2015 IEEE 26th International Symposium on, IEEE, 2015

Abstract: In the context of Scaled Agile Framework (SAFe), software development effort estimation represents a critical task. Proper estimation of the development effort enables the development team to plan the development tasks and predict the amount of time and cost required to deliver software products. Additionally, it allows the development team to determine if the staffing level is sufficient (i.e., team's capacity) during the Program Iteration (PI) planning. Background: SAFe is a framework for scaling Agile across an enterprise. At the development team level, it provides guidance to define, build, test, and deploy software solutions in a short iteration timebox. Agile teams are responsible for delivering software results that meet customer needs and expectations. Part of that responsibility is to estimate the effort of the work they must perform. Agile teams decompose....

Key Word:Scaled Agile Framework; Effort Estimation; Story Points; Fuzzy Logic.

[1]. Sinhal, A., Verma, B. (2013). Software Development effort: A Review. International Journal of Advanced Research in Computer Science and Software Engineering. Vol 3 (6): 1120-1135.
[2]. Avasthy, R., Batra, B., Kundra, H. (2016). A hybrid parametric model for enrichment of software effort estimation. International Journal of Computer Science and Information Security. Vol 14 (10).
[3]. Boemh, B. (1981). Software Engineering Economics. Englewood Cliffs, NJ, Prentice Hall.
[4]. Chawla, R., Ahlawat, D. (2015). Software development effort using fuzzy logic framework – An Implementation. International Journal on Advanced Computer Theory and Engineering. Vol 4 (4).
[5]. Kamal, S., Kamal, Z., Khan, S., Nasil, J.A. (2013). A fuzzy logic-based software estimation model. International Journal of Software Engineering and Its Applications. Vol 7, No 2.

Abstract: In article 271 RV, it states that the plaintiff may withdraw his case, the revocation of the case can be held before the response. After the response was given, the revocation will be approved with the agreement of the defendant. The regulation of this article to protect the need of the defendant's right in order to make the plaintiff does not arbitrarily act to the defendant. In reality in the Shariaa Court of Banda Aceh, after the law revocation has been decided by both of the plaintiff and the defendant to end the dispute through the reconciliation with the agreement of the defendant, the complainant revoke his suit. This research used normative law research with qualitative analysis which....

Key Word: Decision, Suit Revocation, Agreement, Sharia Court of Banda Aceh

[1]. Abdulkadir Muhammad, HukumAcaraPerdata Indonesia, (Citra AdityaBakti, Bandung, 1992).
[2]. Ibrahim Husein, FiqhPerbandinganDalamKhoirulAnam, CO1206080, TinjauanYuridisTentangPencabutan GugatanPerkara CeraiGugat Di PengadilanTinggi Agama Surabaya (putusan No. 96/Pdt.G/2009/PTA.Sby), (Skripsi, FakultasSyari‟ah, Institut Agama Islam SunanAmpel, Surabaya, 2010).
[3]. M. YahyaHarahap,HukumAcaraPerdata, TentangGugatan, Persidangan, Penyitaan, Pembuktian, danPutusanPengadilan, Cet. 1, (SinarGrafika,Jakarta, 2005).
[4]. SudiknoMertokusumo, HukumAcaraPerdataPengadilanNegeri, Cet. Pertama, (PenerbitBinaCipta, Jakarta, 1997).
[5]. ---------- Mertokusumo, HukumAcaraPerdata Indonesia, (CahayaAtmaPustaka, Yokyakarta, 2013).

Abstract: The Internet of things, also known as IoT, is a connected system consisting of interrelated devices used for computing purposes, mechanical machines, digital machines, various objects, components, animals or human beings, where all of them are provided with a certain unique identifying identity called UID. This provides the users with the ability to transfer data over a large and connected network without the requirement of human-to-human kind of communication or human-to-computer interaction.Internet of Things is a virtual ecosystem consisting of physical objects connected over internet and is accessible through an internet connection. The embedded technology used in the IoT...........

Key Word: Internet of Things, accident detection, sensor nodes

[1]. Zanella, A., Angelo, C.: Internet of things for smart cities. Int. J. Internet of Things. 1 (2014) 22–32
[2]. Chen, S, Xui, H.: A vision of Iot. Int. J. Internet of Things. 1 (2014) 349–359
[3]. Stankovic, J. A.:Research directions for the Internet of Things. IEEE Internet Things J., 1(2014),pp. 3-9
[4]. Liu, T. :The application and development of IoT. Proc. Int. Symp. Inf. Technol. Med. Educ. (ITME), 2(2012),991-994
[5]. Zanella, A.:Internet of Things for smart cities. IEEE Internet Things J.,1(2014),22-32

Abstract: Given the ubiquity of social media, interest-based interactivity as a key component to enhance user experience. Interest-based interactivity modelling is extracted from user interaction in a cross-platform social media Big Data repository. The aim of this study is two things: first to address theoretical dilemmas of a cross-platform user experience; second, implement an platform for providing recommendation across different social network based on user interest. Use case is based on a cross-platform interest-based navigation and content filtering across multiple social content streams. The streams consisted of tags from social media content through a discovery process. Tested the application on a social media content streams to generate a Big Data scenario.

Key Word: Interactivity, Recommendation Based, Cross Platform, Big Data Repository, User Experience

[1]. Zaharieva,M.,DelFabro,M.,&Zeppelzauer,M.(2015).Cross-PlatformSocialEventDetection.IEEEMultiMedia,1–1.doi:10.1109/mmul.2015.57
[2]. Still,K.,Isomursu,M.,Still,J.,&Isomursu,P.(n.d.).TheRoleofTechnologyinInterest-BasedCommunities:Interactivity,ImmersionandConnectivity.
[3]. IEEE/WIC/ACM International Conference on Web Intelligence (WI'04). doi:10.1109/wi.2004.10168
[4]. MingYan,JitaoSang,TaoMei,&ChangshengXu.(2013).Friendtransfer:Cold-startfriendrecommendationwithcross-platformtransferlearningofsocial knowledge. 2013 IEEE International Conference on Multimedia and Expo (ICME).doi:10.1109/icme.2013.6607510
[5]. Niveditha N M (Computer Science & Engineering, VTU, Belgaum) Design an Interest Based Interactivity Platform for Social Media through Big Data International Journal of Advanced Engineering Research and Technology (IJAERT) Volume 3 Issue 2, February 2015, ISSN No.: 2348 –8190

Abstract: Illegal and criminal systems have become a widespread reality today, so as to favor the establishment of criminal mafia infiltrations within the Municipalities. In this study, a predictive algorithm on mafia criminal infiltrations is proposed that can lead to the dissolution of a municipal administration. Some variables are considered two types of variables: numeric and boolean. The first case includes variables such as the number of inhabitants, bars, earth moving companies, banks, supermarkets and more. The second case includes variables such as cases of murder, attacks, drug dealing, extortion. The prediction is realized through Machine Learning techniques, in particular through a logistic regression model.

Key Word: Criminal mafia infiltrations; Machine Learning; Logistic Regression Model

[1]. Binder, J., Koller, D., Russell, S. J., and Kanazawa, K. (1997a). Adaptive probabilistic networks with hidden variables. Machine Learning, 29, 213-244.
[2]. Calderoni, F., Caneppele, S. (2009). The criminal geography of procurement. The infiltration of organized crime in public procurement in the South of Italy. Franco Angeli Publisher
[3]. Lauritzen, S. (1995). The EM algorithm for graphi- cal association models with missing data. Computa- tional Statistics and Data Analysis, 19, 191-201.
[4]. Russell, S. J., Binder, J., Koller, D., and Kanazawa, K. (1995). Local learning in probabilistic networks with hidden variables. In IJCAI-95, pp. 1146-52.

Abstract: This work will address the issue of corporate access security. One of today's problems affecting companies around the world. This study will cover both access to the "main door" and "internal company access". The accesses to the "main door" will be monitored by a Face Recognition system, while the internal accesses will be monitored by the Cloud thanks to the Arduino Beacon technology installed on each internal door of the company.This work will therefore deal with the concepts of Face Detection and Face Recognition, thus creating a system that will perform better in the corporate field. The core of the work is focused on the explanation, implementation and testing of the techniques used with the help of the "CORDOBA dataset" created specifically for the company. This dataset will also contain faces that will be taken from the CASIA-WebFace online public dataset.....

Key Word:Face Recognition, Face Detection, Company.CORDOBA, Physical Access Management Control System

[1]. W. Zhao, R. chellappa e P. Phillips, «ACM Computing Surveys (CSUR), » Face recognition: A literature survey, December 2003.
[2]. A. Suman, «Automated face recognition: Applications within law enforcement, » Market and technology review, NPIA, 2006.
[3]. S. K. A. T. K. T. Talele, «Efficient Face Detection using Adaboost,» IJCA Proc on International Conference in Computational Intelligence, 2012.
[4]. T. Mita, T. Kaneko e O. Hori, «Joint Haar-like Features for Face Detection,» Proceedings of the Tenth IEEE International Conference on Computer Vision, 2005.
[5]. T. Ahonen, A. Hadid e M. Peitikainen, «Face recognition with local binary patterns,» In Proc. of European Conference of Computer Vision, 2004.

Abstract: Education is the most important factor for shaping the personality of an individual. Development of a country depends on the upgradation of education system.Due to the large volume of educational data in higher education, it is challenging to predict the academic performance of students.In this paper authors presentseven built in classifiers namely J48, Random Forest, Rap Tree, LMT, Naïve Bayes, BayesNet and PARTwith the questionnaires filled up by final year students of Computer Science and Engineering Department,Assam Kaziranga University, Jorhat, Assam and suggests the efficient algorithm based on certain parameters. The survey was done based on total 22 questionnaires and 152 responses. In the whole analysis process WEKA 3.8 was used and Random Forest algorithm was found as the most efficient algorithm among all the considered algorithms.

Key Word: WEKA, Prédiction, J48, Random Forest, Rap Tree, LMT, Naïve Bayes, BayesNet, PART

[1]. BakerRSJd, Yacef K.The state of educational datamining in 2009: A review and future visions. J EduData Min 2009.
[2]. Khasanah, A.U. and Harwati, A Comparative Study to Predict Student's Performance Using Educational Data Mining Techniques. IOP Conf. Series: Materials Science and Engineering, 2017. 215(012036): p. 7.
[3]. NikitabenShelke and ShriniwasGadage, "A survey of data mining approaches in performance analysis and evaluation", International Journal of Advanced Research in Computer Science and Software Engineering ,vol 5, iss 4, 2015.
[4]. Nichat, A.A. and D.A.B. Raut, Analysis of Student Performance Using Data Mining Technique. International Journal of Innovative Research in Computer and Communication Engineering, 2017. 2007(An ISO 3297): p. 5.
[5]. V. Hegde, "Dimensionality Reduction Technique for Developing Undergraduate Student Dropout Model using Principal Component Analysis through R Package," pp. 1–6, 2016.

Abstract: The role ofcryptography has become a major part of every modern software and network architecture.Most programming languages, software frameworks and application libraries do not provide a cryptographyhierarchy and do not even implement a basic cryptography model, which makes the development of asoftware system or communication network even harderfrom a security point of view.This paper presents an object-oriented solution for providing all the main classifications of cryptography algorithms via a hierarchy of classes that offer frequently used formats and security features for both in-transit and at-restdata security. It also presents the important role of all three main types of cryptographic algorithms for the security and development of any state-of-the-artsystem.The purpose of the developed and published online practical realization of a cryptography hierarchy, for a language that has no embedded cryptography model, is to emphasize and show the need for such components and their usage forauthentication, integrity verification, secure storage and safe data transfers.This article......

Keywords: cryptography,security,object-oriented hierarchy, cryptography model,at-rest, in-transit, network communication,verification, identification,authentication, authorization, data, hash functions, symmetric encryption, asymmetric encryption, digital signature, authenticated encryption,digital envelope,key exchange, message digestion, digest, salt, secret key, key management, initialization vector, block, mode of operation, padding standard,public key, private key, key pair, MAC, HMAC,KDF, HKDF, PBKDF2, MD5, SHA-1, SHA-2, SHA-3, Whirlpool, Argon2, Bcrypt, AES, CAMELLIA,RSA, DSA.

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