Search results for: silicone data cable

Authors: G. Julius P. Roy, Prateek Saxena, Sanjeev Singh

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Power distribution utilities two decades ago would collect data from its customers not later than a period of at least one month. The origin of SmartGrid and AMI has subsequently increased the sampling frequency leading to 1000 to 10000 fold increase in data quantity. This increase is notable and this steered to coin the tern Big Data in utilities. Power distribution industry is one of the largest to handle huge and complex data for keeping history and also to turn the data in to significance. Majority of the utilities around the globe are adopting SmartGrid technologies as a mass implementation and are primarily focusing on strategic interdependence and synergies of the big data coming from new information sources like AMI and intelligent SCADA, there is a rising need for new models of data management and resurrected focus on analytics to dissect data into descriptive, predictive and dictatorial subsets. The goal of this paper is to is to bring load disaggregation into smart energy toolkit for commercial usage.

Keywords: data management, analytics, energy data analytics, smart grid, smart utilities

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Authors: P. Srinivasa Rao, K. Venkatesh Sharma, G. Sadhya Devi, V. Nagesh

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Privacy Preserving Data Publication is the main concern in present days because the data being published through the internet has been increasing day by day. This huge amount of data was named as Big Data by its size. This project deals the privacy preservation in the context of Big Data using a data warehousing solution called hive. We implemented Nearest Similarity Based Clustering (NSB) with Bottom-up generalization to achieve (v,l)-anonymity. (v,l)-Anonymity deals with the sensitivity vulnerabilities and ensures the individual privacy. We also calculate the sensitivity levels by simple comparison method using the index values, by classifying the different levels of sensitivity. The experiments were carried out on the hive environment to verify the efficiency of algorithms with Big Data. This framework also supports the execution of existing algorithms without any changes. The model in the paper outperforms than existing models.

Keywords: sensitivity, sensitive level, clustering, Privacy Preserving Data Publication (PPDP), bottom-up generalization, Big Data

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Authors: Behnam Tavakkol

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Uncertain data mining algorithms use different ways to consider uncertainty in data such as by representing a data object as a sample of points or a probability distribution. Fuzzy methods have long been used for clustering traditional (certain) data objects. They are used to produce non-crisp cluster labels. For uncertain data, however, besides some uncertain fuzzy k-medoids algorithms, not many other fuzzy clustering methods have been developed. In this work, we develop a fuzzy kernel k-medoids algorithm for clustering uncertain data objects. The developed fuzzy kernel k-medoids algorithm is superior to existing fuzzy k-medoids algorithms in clustering data sets with non-linearly separable clusters.

Keywords: clustering algorithm, fuzzy methods, kernel k-medoids, uncertain data

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Authors: Mehdi Radmehr, Amir Hamed Mashhadzadeh, Mehdi Jafari

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Traditional HVDC systems are tough to DC short circuits as they are current regulated with a large reactance connected in series with cables. Multi-terminal DC wind farm topologies are attracting increasing research attempt. With AC/DC converters on the generator side, this topology can be developed into a multi-terminal DC network for wind power collection, which is especially suitable for large-scale offshore wind farms. For wind farms, the topology uses high-voltage direct-current transmission based on voltage-source converters (VSC-HVDC). Therefore, they do not suffer from over currents due to DC cable faults and there is no over current to react to. In this study, the multi-terminal DC wind farm topology is introduced. Then, possible internal DC faults are analyzed according to type and characteristic. Fault over current expressions are given in detail. Under this characteristic analysis, fault detection and detailed protection methods are proposed. Theoretical analysis and PSCAD/EMTDC simulations are provided.

Keywords: DC short circuits, multi-terminal DC wind farm topologies, HVDC transmission based on VSC, fault analysis

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Authors: Nirmala Gopal, Sheetal Bhoola, Audecious Mugwagwa

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This paper discusses the continued infringement and exploitation of data by non-state actors for destructive purposes, emphasizing radical terrorist organizations. It will discuss how terrorist organizations access and use data to foster their nefarious agendas. It further examines how cybersecurity, designed as a tool to curb data exploitation, is ineffective in raising global citizens' concerns about how their data can be kept safe and used for its acquired purpose. The study interrogates several policies and data protection instruments, such as the Data Protection Act, Cyber Security Policies, Protection of Personal Information(PPI) and General Data Protection Regulations (GDPR), to understand data use and storage in democratic states. The study outcomes point to the fact that international cybersecurity and cybercrime legislation, policies, and conventions have not curbed violations of data access and use by radical terrorist groups. The study recommends ways to enhance cybersecurity and reduce cyber risks using democratic principles.

Keywords: cybersecurity, data exploitation, terrorist organizations, data democracy

Procedia PDF Downloads 173

Authors: Eugenia Jilinguirian

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In the healthcare industry, data mining is essential since it transforms the field by collecting useful data from large datasets. Data mining is the process of applying advanced analytical methods to large patient records and medical histories in order to identify patterns, correlations, and trends. Healthcare professionals can improve diagnosis accuracy, uncover hidden linkages, and predict disease outcomes by carefully examining these statistics. Additionally, data mining supports personalized medicine by personalizing treatment according to the unique attributes of each patient. This proactive strategy helps allocate resources more efficiently, enhances patient care, and streamlines operations. However, to effectively apply data mining, however, and ensure the use of private healthcare information, issues like data privacy and security must be carefully considered. Data mining continues to be vital for searching for more effective, efficient, and individualized healthcare solutions as technology evolves.

Keywords: data mining, healthcare, big data, individualised healthcare, healthcare solutions, database

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Authors: Yunus Doğan, Ahmet Durap

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Coastal regions are the one of the most commonly used places by the natural balance and the growing population. In coastal engineering, the most valuable data is wave behaviors. The amount of this data becomes very big because of observations that take place for periods of hours, days and months. In this study, some statistical methods such as the wave spectrum analysis methods and the standard statistical methods have been used. The goal of this study is the discovery profiles of the different coast areas by using these statistical methods, and thus, obtaining an instance based data set from the big data to analysis by using data mining algorithms. In the experimental studies, the six sample data sets about the wave behaviors obtained by 20 minutes of observations from Mersin Bay in Turkey and converted to an instance based form, while different clustering techniques in data mining algorithms were used to discover similar coastal places. Moreover, this study discusses that this summarization approach can be used in other branches collecting big data such as medicine.

Keywords: clustering algorithms, coastal engineering, data mining, data summarization, statistical methods

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Authors: Mehrdad Gholami, Shima Behkami, Sharifuddin B. Md. Zain, Firdaus A. B. Kamaruddin

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Starting from the first application of a microwave oven for sample preparation in 1975 for the purpose of wet ashing of biological samples using a domestic microwave oven, many microwave-assisted dissolution vessels have been developed. The advanced vessels are armed with special safety valve that release the excess of pressure while the vessels are in critical conditions due to applying high power of microwave. Nevertheless, this releasing of pressure may cause lose of volatile elements. In this study Teflon bottles are designed with relatively thicker wall compared to commercial ones and a silicone based polymer was used to prepare an O-ring which plays the role of safety valve. In this design, eight vessels are located in an ABS holder to keep them stable and safe. The advantage of these vessels is that they need only 2 mL of HNO3 and 1mL H2O2 to digest different environmental samples, namely, sludge, apple leave, peach leave, spinach leave and tomato leave. In order to investigate the performance of this design an ICP-MS instrument was applied for multi elemental analysis of 20 elements on the SRM of above environmental samples both using this design and a commercial microwave digestion design. Very comparable recoveries were obtained from this simple design with the commercial one. Considering the price of ultrapure chemicals and the amount of them which normally is about 8-10 mL, these simple vessels with the procedures that will be discussed in detail are very cost effective and very suitable for environmental studies.

Keywords: inductively coupled plasma mass spectroscopy (ICP-MS), PTFE vessels, Teflon bombs, microwave digestion, trace element

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Authors: Anny Retnowati, Elisabeth Sundari

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This research aims to elaborate the meaning of personal data protection principles on patient access to health data in medical records in Indonesia and its implications. The method uses normative legal research by examining health law in Indonesia regarding the patient's right to access their health data in medical records. The data will be analysed qualitatively using the interpretation method to elaborate on the limitation of the meaning of personal data protection principles on patients' access to their data in medical records. The results show that patients only have the right to obtain copies of their health data in medical records. There is no right to inspect directly at any time. Indonesian health law limits the principle of patients' right to broad access to their health data in medical records. This restriction has implications for the reduction of personal data protection as part of human rights. This research contribute to show that a limitaion of personal data protection may abuse the human rights.

Keywords: access, health data, medical records, personal data, protection

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Authors: Martin Böhmer, Agatha Dabrowski, Boris Otto

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The trend of digitization significantly changes the role of data for enterprises. Data turn from an enabler to an intangible organizational asset that requires management and qualifies as a tradeable good. The idea of a networked economy has gained momentum in the data domain as collaborative approaches for data management emerge. Traditional organizational knowledge consequently needs to be extended by comprehensive knowledge about data. The knowledge about data is vital for organizations to ensure that data quality requirements are met and data can be effectively utilized and sovereignly governed. As this specific knowledge has been paid little attention to so far by academics, the aim of the research presented in this paper is to conceptualize it by proposing a “data knowledge model”. Relevant model entities have been identified based on a design science research (DSR) approach that iteratively integrates insights of various industry case studies and literature research.

Keywords: data management, digitization, industry 4.0, knowledge engineering, metamodel

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Authors: G. Punithavathy

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The research proposes a statistical model for the distributed acoustic sensor interrogation units that broadcast a laser pulse into the fiber optics, where interactions within the fiber determine the localized acoustic energy that causes light reflections known as backscatter. The backscattered signal's amplitude and phase can be calculated using explicit equations. The created model makes amplitude signal spectrum and autocorrelation predictions that are confirmed by experimental findings. Phase signal characteristics that are useful for researching optical time domain reflectometry (OTDR) system sensing applications are provided and examined, showing good agreement with the experiment. The experiment was successfully done with the use of Python coding. In this research, we can analyze the entire distributed acoustic sensing (DAS) component parts separately. This model assumes that the fiber is in a static condition, meaning that there is no external force or vibration applied to the cable, that means no external acoustic disturbances present. The backscattered signal consists of a random noise component, which is caused by the intrinsic imperfections of the fiber, and a coherent component, which is due to the laser pulse interacting with the fiber.

Keywords: distributed acoustic sensing, optical fiber devices, optical time domain reflectometry, Rayleigh scattering

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Authors: J-C. Leneveu, A. Chereau, L. Mansart, T. Mesbah, M. Wyka

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Extracting and interpreting information from Big Data represent a stake for years to come in several sectors such as finance. Currently, numerous methods are used (such as Technical Analysis) to try to understand and to anticipate market behavior, with mixed results because it still seems impossible to exactly predict a financial trend. The increase of available data on Internet and their diversity represent a great opportunity for the financial world. Indeed, it is possible, along with these standard financial data, to focus on exogenous data to take into account more macroeconomic factors. Coupling the interpretation of these data with standard methods could allow obtaining more precise trend predictions. In this paper, in order to observe the influence of exogenous data price independent of other usual effects occurring in classical markets, behaviors of Bitcoin users are introduced in a model reconstituting Bitcoin value, which is elaborated and tested for prediction purposes.

Keywords: big data, bitcoin, data mining, social network, financial trends, exogenous data, global economy, behavioral finance

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Authors: Jeroen S. de Bruin, Karin Schindler, Christian Schuh

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With increasingly more mobile health applications appearing due to the popularity of smartphones, the possibility arises that these data can be used to improve the medical diagnostic process, as well as the overall quality of healthcare, while at the same time lowering costs. However, as of yet there have been no reports of a successful combination of patient-generated data from smartphones with data from clinical routine. In this paper, we describe how these two types of data can be combined in a secure way without modification to hospital information systems, and how they can together be used in a medical expert system for automatic nutritional classification and triage.

Keywords: mobile health, data integration, expert systems, disease-related malnutrition

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Authors: Mohammad Mohammadzadeh, Alireza Ghasempour

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In this paper, a new design of monopole antenna has been proposed that increases the contrast of intravascular magnetic resonance images through increasing the homogeneity of the intrinsic signal-to-noise ratio (ISNR) distribution around the antenna. The antenna is made of a coaxial cable with three parasitic elements. Lengths and positions of the elements are optimized by the improved genetic algorithm (IGA) for 1.5, 3, 4.7, and 7Tesla MRI systems based on a defined cost function. Simulations were also conducted to verify the performance of the designed antenna. Our simulation results show that each time IGA is executed different values for the parasitic elements are obtained so that the cost functions of those antennas are high. According to the obtained results, IGA can also find the best values for the parasitic elements (regarding cost function) in the next executions. Additionally, two dimensional and one-dimensional maps of ISNR were drawn for the proposed antenna and compared to the previously published monopole antenna with one parasitic element at the frequency of 64MHz inside a saline phantom. Results verified that in spite of ISNR decreasing, there is a considerable improvement in the homogeneity of ISNR distribution of the proposed antenna so that their multiplication increases.

Keywords: intravascular MR antenna, monopole antenna, parasitic elements, signal-to-noise ratio (SNR), genetic algorithm

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Authors: Sombol Mokhles

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This paper tries to show the prospects of utilising (big)data for enabling just the transition of diverse cities. Our key purpose is to offer a framework of applications and implications of utlising (big) data in comparing sustainability transitions across different cities. Relying on the cosmopolitan comparison, this paper explains the potential application of (big) data but also its limitations. The paper calls for adopting a data-driven and just perspective in including different cities around the world. Having a just and inclusive approach at the front and centre ensures a just transition with synergistic effects that leave nobody behind.

Keywords: big data, just sustainable transition, cosmopolitan city comparison, cities

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Authors: Modesta E. Ezema, Christopher C. Ezema, Christian C. Ugwu, Udoka F. Eze, Florence M. Babalola

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Some employees knowingly or unknowingly contribute to loss of data and also expose data to threat in the process of getting their jobs done. Many organizations today are faced with the challenges of how to secure their data as cyber criminals constantly devise new ways of attacking the organization’s secret data. However, this paper enlists the latest strategies that must be put in place in order to protect these important data from being attacked in a collaborative work place. It also introduces us to Advanced Persistent Threats (APTs) and how it works. The empirical study was conducted to collect data from the employee in data centers on how data could be protected from malicious codes and cyber criminals and their responses are highly considered to help checkmate the activities of malicious code and cyber criminals in our work places.

Keywords: data, employee, malware, work place

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Authors: Jia Yi Yap, Angela S. H. Lee

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With the use of big data technologies, organization can get result that they are interested in. Big data technologies simply load all the data that is useful for the organizations and provide organizations a better way of analysing data. The purpose of this research is to get employees’ opinion from films in Malaysia to explore the use of big data technologies in their organization in order to provide how it may affect the perception of the employees on job performance. Therefore, in order to identify will accepting big data technologies in the organization affect the perception of the employee, questionnaire will be distributed to different employee from different Small and medium-sized enterprises (SME) organization listed in Malaysia. The conceptual model proposed will test with other variables in order to see the relationship between variables.

Keywords: big data technologies, employee, job performance, questionnaire

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Authors: N. Musayeva, N. Rustamova, N. Bagirov, S. Ibadov

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Purpose: to analyze the long-term results of external dacryocystorhinostomy (DCR), which remains the preferred primary procedure in the surgical treatment of lacrimal duct obstruction in chronic dacryocystitis. Methodology: long-term results of external DCR (after 3 years) performed on 90 patients (90 eyes) with chronic dacryocystitis from 2018 to 2020 were evaluated. The Azerbaijan National Center of Ophthalmology, named after acad. Zarifa Aliyeva. 15 of the patients were men, 75 – women. The average age was 45±3.2 years. Surgical operations were performed under local anesthesia. All patients suffered from diabetes mellitus for more than 3 years. All patients underwent external DCR and silicone drainage (tube) was implanted. In the postoperative period (after 3 years), lacrimation, purulent discharge, and the condition of the scar at the operation site were assessed. Results: All patients were under observation for more than 18 months. In general, the effectiveness of the surgical operation was 93.34%. Recurrence of disease was observed in 6 patients and in 3 patients (3.33%), the scar at the site of the operation was rough (non-cosmetic). In 3 patients (3.33%) – the surgically formed anastomosis between the lacrimal sac and the nasal bone was obstructed by scar tissue. These patients were reoperated by trans canalicular laser DCR. Conclusion: Despite the long-term (more than a hundred years) use of external DCR, it remains one of the primary techniques in the surgery of chronic dacryocystitis. Due to the high success rate and good long-term results of DCR in the treatment of chronic dacryocystitis in patients suffering from diabetes mellitus, we recommend external DCR for this group of patients.

Keywords: chronic dacryocystitis, diabetes mellitus, external dacryocystorhinostomy, long-term results

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Authors: Beulah Rani Inbanathan

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In the present era, it is vivid from the numerous outcomes that data privacy is being compromised in various ways. Machine learning is one technology that uses the centralized server, and then data is given as input which is being analyzed by the algorithms present on this mentioned server, and hence outputs are predicted. However, each time the data must be sent by the user as the algorithm will analyze the input data in order to predict the output, which is prone to threats. The solution to overcome this issue is federated learning, where the models alone get updated while the data resides on the local machine and does not get exchanged with the other local models. Nevertheless, even on these local models, there are chances of data poisoning, and it is crystal clear from various experiments done by many people. This paper delves into many ways where data poisoning occurs and the many methods through which it is prevalent that data poisoning still exists. It includes the poisoning attacks on IoT devices, Edge devices, Autoregressive model, and also, on Industrial IoT systems and also, few points on how these could be evadible in order to protect our data which is personal, or sensitive, or harmful when exposed.

Keywords: data poisoning, federated learning, Internet of Things, edge computing

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Authors: Nasibullina A., Leonov D.

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The ultrasonography method is actively used for the early diagnosis of various le-sions in the human body, including the mammary gland. The incidence of breast cancer has increased by more than 20%, and mortality by 14% since 2008. The correctness of the diagnosis often directly depends on the qualifications and expe-rience of a diagnostic medical sonographer. That is why special attention should be paid to the practical training of future specialists. Anatomical phantoms are ex-cellent teaching tools because they accurately imitate the characteristics of real hu-man tissues and organs. The purpose of this work is to create a breast phantom for practicing ultrasound diagnostic skills in grayscale and elastography imaging, as well as ultrasound-guided biopsy sampling. We used silicone-like compounds ranging from 3 to 17 on the Shore scale hardness units to simulate soft tissue and lesions. Impurities with experimentally selected concentrations were added to give the phantom the necessary attenuation and reflection parameters. We used 3D modeling programs and 3D printing with PLA plastic to create the casting mold. We developed a breast phantom with inclusions of varying shape, elasticity and echogenicity. After testing the created phantom in B-mode and elastography mode, we performed a survey asking 19 participants how realistic the sonograms of the phantom were. The results showed that the closest to real was the model of the cyst with 9.5 on the 0-10 similarity scale. Thus, the developed breast phantom can be used for ultrasonography, elastography, and ultrasound-guided biopsy training.

Keywords: breast ultrasound, mammary gland, mammography, training phantom, tissue-mimicking materials

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Authors: R. M. Kalayappan, N. Kathiravan

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In automobile industries, Controller Area Network (CAN) is widely used to reduce the system complexity and inter-task communication. Therefore, this paper proposes the hardware implementation of data frame communication between one controller to other. The CAN data frames and protocols will be explained deeply, here. The data frames are transferred without any collision or corruption. The simulation is made in the KEIL vision software to display the data transfer between transmitter and receiver in CAN. ARM7 micro-controller is used to transfer data’s between the controllers in real time. Data transfer is verified using the CRO.

Keywords: control area network (CAN), automotive electronic control unit, CAN 2.0, industry

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Authors: Rajbir Cheema

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In order to introduce an integrated research information system, this will provide scientific institutions with the necessary information on research activities and research results in assured quality. Since data collection, duplication, missing values, incorrect formatting, inconsistencies, etc. can arise in the collection of research data in different research information systems, which can have a wide range of negative effects on data quality, the subject of data quality should be treated with better results. This paper examines the data quality problems in research information systems and presents the new techniques that enable organizations to improve their quality of research information.

Keywords: Research information systems (RIS), research information, heterogeneous sources, data quality, data cleansing, science system, standardization

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Authors: Carla Silva

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Recent development of information and communication technology enables us to acquire, collect, analyse data in various fields of socioeconomic – technological systems. Along with the increase of economic globalization and the evolution of information technology, data mining has become an important approach for economic data analysis. As a result, there has been a critical need for automated approaches to effective and efficient usage of massive amount of educational data, in order to support institutions to a strategic planning and investment decision-making. In this article, we will address data from several different perspectives and define the applied data to sciences. Many believe that 'big data' will transform business, government, and other aspects of the economy. We discuss how new data may impact educational policy and educational research. Large scale administrative data sets and proprietary private sector data can greatly improve the way we measure, track, and describe educational activity and educational impact. We also consider whether the big data predictive modeling tools that have emerged in statistics and computer science may prove useful in educational and furthermore in economics. Finally, we highlight a number of challenges and opportunities for future research.

Keywords: data mining, research analysis, investment decision-making, educational research

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Authors: Naik Muhammad, Zahid Ullah, Dong-Ho Choi

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Submerged floating tunnel (SFT) is a new solution for the transportation infrastructure through sea straits, fjords, and inland waters, and can be a good alternative to long span suspension bridges. SFT is a massive cylindrical structure that floats at a certain depth below the water surface and subjected to extreme environmental conditions. The identification of dominant structural response of SFT becomes more important due to intended environmental conditions for the design of SFT. The time domain dynamic problem of SFT moored by vertical and inclined mooring cables/anchors is formulated. The dynamic time history analysis of SFT subjected to hydrodynamic and seismic excitations is performed. The SFT is modeled by finite element 3D beam, and the mooring cables are modeled by truss elements. Based on the dynamic time history analysis the displacements and internal forces of SFT were calculated. The response of SFT is presented for hydrodynamic and seismic excitations. The transverse internal forces of SFT were the maximum compared to vertical direction, for both hydrodynamic and seismic cases; this indicates that the cable system provides very small stiffness in transverse direction as compared to vertical direction of SFT.

Keywords: submerged floating tunnel, hydrodynamic analysis, time history analysis, seismic response

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Authors: Minsung Kim, Jinkoo Kim

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In order to reduce or eliminate seismic damage in structures, many researchers have investigated various energy dissipation devices. In this study, the seismic capacity and cost of a slit-viscoelastic seismic retrofit system composed of a steel slit plate and viscoelastic dampers connected in parallel are evaluated. The combination of the two different damping mechanisms is expected to produce enhanced seismic performance of the building. The analysis model of the system is first derived using various link elements in the nonlinear dynamic analysis software Perform 3D, and fragility curves of the structure retrofitted with the dampers are obtained using incremental dynamic analyses. The analysis results show that the displacement of the structure equipped with the hybrid dampers is smaller than that of the structure with slit dampers due to the enhanced self-centering capability of the system. It is also observed that the initial cost of hybrid system required for the seismic retrofit is smaller than that of the structure with viscoelastic dampers. Acknowledgement: This research was financially supported by the Ministry of Trade, Industry and Energy(MOTIE) and Korea Institute for Advancement of Technology(KIAT) through the International Cooperative R&D program(N043100016_Development of low-cost high-performance seismic energy dissipation devices using viscoelastic material).

Keywords: damped cable systems, seismic retrofit, viscous dampers, self-centering

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Authors: Digvijaysingh S. Bana, Kiran R. Trivedi

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This paper introduces various methods for the alpha wave to detect the difference between two states of brain. One healthy subject participated in the experiment. EEG was measured on the forehead above the eye (FP1 Position) with reference and ground electrode are on the ear clip. The data samples are obtained in the form of EEG raw data. The time duration of reading is of one minute. Various test are being performed on the alpha band EEG raw data.The readings are performed in different time duration of the entire day. The statistical analysis is being carried out on the EEG sample data in the form of various tests.

Keywords: electroencephalogram(EEG), biometrics, authentication, EEG raw data

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Authors: Chhavi Rana

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The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, Healthcare, and business intelligence contain voluminous and incremental data, which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organization's decision-making strategy can be enhanced using big data analytics and applying different machine learning techniques and statistical tools on such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates on various frameworks in the process of Analysis using different machine-learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

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Authors: Chhavi Rana

Abstract:

The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, healthcare, and business intelligence contain voluminous and incremental data which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organisation decision-making strategy can be enhanced by using big data analytics and applying different machine learning techniques and statistical tools to such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates various frameworks in the process of analysis using different machine learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

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Authors: Ji Eun Shin, Dong Hoon Lim

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Data clustering is a common technique used in data analysis and is used in many applications, such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing. K-means clustering is a well-known clustering algorithm aiming to cluster a set of data points to a predefined number of clusters. In this paper, we implement K-means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. The main idea is to introduce a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. The experimental results demonstrated that K-means algorithm using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also showed that our K-means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases.

Keywords: big data, combiner, K-means clustering, RHadoop

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Authors: Nikita Valluri, Vatcharaporn Esichaikul

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With the popularity of data-driven decision making on the rise, this study focuses on providing an alternative outlook towards the process of decision-making. Combining quantitative and qualitative methods rooted in the social sciences, an integrated framework is presented with a focus on delivering a much more robust and efficient approach towards the concept of data-driven decision-making with respect to not only Big data but also 'Thick data', a new form of qualitative data. In support of this, an example from the retail sector has been illustrated where the framework is put into action to yield insights and leverage business intelligence. An interpretive approach to analyze findings from both kinds of quantitative and qualitative data has been used to glean insights. Using traditional Point-of-sale data as well as an understanding of customer psychographics and preferences, techniques of data mining along with qualitative methods (such as grounded theory, ethnomethodology, etc.) are applied. This study’s final goal is to establish the framework as a basis for providing a holistic solution encompassing both the Big and Thick aspects of any business need. The proposed framework is a modified enhancement in lieu of traditional data-driven decision-making approach, which is mainly dependent on quantitative data for decision-making.

Keywords: big data, customer behavior, customer experience, data mining, qualitative methods, quantitative methods, thick data

Procedia PDF Downloads 135