Search results for: housing data
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 24797

Search results for: housing data

24347 Big Data and Analytics in Higher Education: An Assessment of Its Status, Relevance and Future in the Republic of the Philippines

Authors: Byron Joseph A. Hallar, Annjeannette Alain D. Galang, Maria Visitacion N. Gumabay

Abstract:

One of the unique challenges provided by the twenty-first century to Philippine higher education is the utilization of Big Data. The higher education system in the Philippines is generating burgeoning amounts of data that contains relevant data that can be used to generate the information and knowledge needed for accurate data-driven decision making. This study examines the status, relevance and future of Big Data and Analytics in Philippine higher education. The insights gained from the study may be relevant to other developing nations similarly situated as the Philippines.

Keywords: big data, data analytics, higher education, republic of the philippines, assessment

Procedia PDF Downloads 317
24346 Data Management and Analytics for Intelligent Grid

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

Abstract:

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

Procedia PDF Downloads 761
24345 Privacy Preserving Data Publishing Based on Sensitivity in Context of Big Data Using Hive

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

Abstract:

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

Procedia PDF Downloads 270
24344 A Fuzzy Kernel K-Medoids Algorithm for Clustering Uncertain Data Objects

Authors: Behnam Tavakkol

Abstract:

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

Procedia PDF Downloads 192
24343 Socioeconomic Impact of Capture and Sale of Scylla serrata in Metuge Community

Authors: Siran Offman, TeóFilo Nhamuhuco, EzíDio Cuamba

Abstract:

Scylla serrata is important for livelihood in coastal communities in Metuge District, Northern Mozambique, where the study was conducted from June to August 2014. The aim was to estimate the socioeconomic impact of mangrove crabs captures in Metuge communities. Data was collected based on semi-structured questionnaire in the landing sites and in local crab markets. In total were inquired 26 crab collectors and 6 traders, this activity is practiced only by men, with ages ranging from 15 to 68 years old. To capture the crab the collectors use a long iron hook with 1.5-2 meters, during 5-7 times per week, spending about 5-8 hours a day. The captured varied from 2-20 kg per day. In the village 1 kg costs 1-1.5 USD and 3 USD applied by traders who sell along the streets, for tourists and specific customers from Asia, where the traders can sell until 50 kg.The incomes vary from 11-174USD per month. The value chain between the collectors and trader is unreasonable, as the second makes less effort and earns more, thereby the socio-economic impact is observed, however not high for the collectors, as the money is intended to purchase food and agricultural instruments. In another hand, 90% of collectors dropped out the school, and the money does not have a great impact as they still have precarious housing, rely on community wells to access water, do not have electric power and possess high number of family members.

Keywords: socio-economic and of, impacts, capture, sale, Scylla serrata

Procedia PDF Downloads 209
24342 Democracy Bytes: Interrogating the Exploitation of Data Democracy by Radical Terrorist Organizations

Authors: Nirmala Gopal, Sheetal Bhoola, Audecious Mugwagwa

Abstract:

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

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24341 Healthcare Data Mining Innovations

Authors: Eugenia Jilinguirian

Abstract:

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

Procedia PDF Downloads 46
24340 Summarizing Data Sets for Data Mining by Using Statistical Methods in Coastal Engineering

Authors: Yunus Doğan, Ahmet Durap

Abstract:

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

Procedia PDF Downloads 341
24339 Access to Health Data in Medical Records in Indonesia in Terms of Personal Data Protection Principles: The Limitation and Its Implication

Authors: Anny Retnowati, Elisabeth Sundari

Abstract:

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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24338 Conceptualizing the Knowledge to Manage and Utilize Data Assets in the Context of Digitization: Case Studies of Multinational Industrial Enterprises

Authors: Martin Böhmer, Agatha Dabrowski, Boris Otto

Abstract:

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

Procedia PDF Downloads 333
24337 Analysis and Forecasting of Bitcoin Price Using Exogenous Data

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

Abstract:

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

Procedia PDF Downloads 337
24336 On the Combination of Patient-Generated Data with Data from a Secure Clinical Network Environment: A Practical Example

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

Abstract:

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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24335 The Prospects of Leveraging (Big) Data for Accelerating a Just Sustainable Transition around Different Contexts

Authors: Sombol Mokhles

Abstract:

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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24334 Strategic Workplace Security: The Role of Malware and the Threat of Internal Vulnerability

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

Abstract:

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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24333 Arc Interruption Design for DC High Current/Low SC Fuses via Simulation

Authors: Ali Kadivar, Kaveh Niayesh

Abstract:

This report summarizes a simulation-based approach to estimate the current interruption behavior of a fuse element utilized in a DC network protecting battery banks under different stresses. Due to internal resistance of the battries, the short circuit current in very close to the nominal current, and it makes the fuse designation tricky. The base configuration considered in this report consists of five fuse units in parallel. The simulations are performed using a multi-physics software package, COMSOL® 5.6, and the necessary material parameters have been calculated using two other software packages.The first phase of the simulation starts with the heating of the fuse elements resulted from the current flow through the fusing element. In this phase, the heat transfer between the metallic strip and the adjacent materials results in melting and evaporation of the filler and housing before the aluminum strip is evaporated and the current flow in the evaporated strip is cut-off, or an arc is eventually initiated. The initiated arc starts to expand, so the entire metallic strip is ablated, and a long arc of around 20 mm is created within the first 3 milliseconds after arc initiation (v_elongation = 6.6 m/s. The final stage of the simulation is related to the arc simulation and its interaction with the external circuitry. Because of the strong ablation of the filler material and venting of the arc caused by the melting and evaporation of the filler and housing before an arc initiates, the arc is assumed to burn in almost pure ablated material. To be able to precisely model this arc, one more step related to the derivation of the transport coefficients of the plasma in ablated urethane was necessary. The results indicate that an arc current interruption, in this case, will not be achieved within the first tens of milliseconds. In a further study, considering two series elements, the arc was interrupted within few milliseconds. A very important aspect in this context is the potential impact of many broken strips parallel to the one where the arc occurs. The generated arcing voltage is also applied to the other broken strips connected in parallel with arcing path. As the gap between the other strips is very small, a large voltage of a few hundred volts generated during the current interruption may eventually lead to a breakdown of another gap. As two arcs in parallel are not stable, one of the arcs will extinguish, and the total current will be carried by one single arc again. This process may be repeated several times if the generated voltage is very large. The ultimate result would be that the current interruption may be delayed.

Keywords: DC network, high current / low SC fuses, FEM simulation, paralle fuses

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24332 Acceptance of Big Data Technologies and Its Influence towards Employee’s Perception on Job Performance

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

Abstract:

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

Procedia PDF Downloads 274
24331 An Exploratory Approach of the Latin American Migrants’ Urban Space Transformation of Antofagasta City, Chile

Authors: Carolina Arriagada, Yasna Contreras

Abstract:

Since mid-2000, the migratory flows of Latin American migrants to Chile have been increasing constantly. There are two reasons that would explain why Chile is presented as an attractive country for the migrants. On the one hand, traditional centres of migrants’ attraction such as the United States and Europe have begun to close their borders. On the other hand, Chile exhibits relative economic and political stability, which offers greater job opportunities and better standard of living when compared to the migrants’ origin country. At the same time, the neoliberal economic model of Chile, developed under an extractive production of the natural resources, has privatized the urban space. The market regulates the growth of the fragmented and segregated cities. Then, the vulnerable population, most of the time, is located in the periphery and in the marginal areas of the urban space. In this aspect, the migrants have begun to occupy those degraded and depressed areas of the city. The problem raised is that the increase of the social spatial segregation could be also attributed to the migrants´ occupation of the marginal urban places of the city. The aim of this investigation is to carry out an analysis of the migrants’ housing strategies, which are transforming the marginal areas of the city. The methodology focused on the urban experience of the migrants, through the observation of spatial practices, ways of living and networks configuration in order to transform the marginal territory. The techniques applied in this study are semi–structured interviews in-depth interviews. The study reveals that the migrants housing strategies for living in the marginal areas of the city are built on a paradox way. On the one hand, the migrants choose proximity to their place of origin, maintaining their identity and customs. On the other hand, the migrants choose proximity to their social and familiar places, generating sense of belonging. In conclusion, the migration as international displacements under a globalized economic model increasing socio spatial segregation in cities is evidenced, but the transformation of the marginal areas is a fundamental resource of their integration migratory process. The importance of this research is that it is everybody´s responsibility not only the right to live in a city without any discrimination but also to integrate the citizens within the social urban space of a city.

Keywords: migrations, marginal space, resignification, visibility

Procedia PDF Downloads 117
24330 Data Poisoning Attacks on Federated Learning and Preventive Measures

Authors: Beulah Rani Inbanathan

Abstract:

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

Procedia PDF Downloads 68
24329 Building Care Networks for Patients with Life-Limiting Illnesses: Perspectives from Health Care and Social Service Providers

Authors: Lindy Van Vliet, Saloni Phadke, Anthea Nelson, Ann Gallant

Abstract:

Comprehensive and compassionate palliative care and support requires an integrated system of care that draws on formal health and social service providers working together with community and informal networks to ensure that patients and families have access to the care they need. The objective of this study is to further explore and understand the community supports, services, and informal networks that health care professionals and social service providers rely on to allow their patients to die in their homes and communities. Drawing on an interpretivist, exploratory, qualitative design, our multidisciplinary research team (medicine, nursing and social work) conducted interviews with 15 health care and social service providers in the Ottawa region. Interview data was audio-recorded, transcribed and analyzed using a reflexive thematic analysis approach. The data deepens our understandings of the facilitators and barriers that arise as health care and social service providers attempt to build networks of care for patients with life limiting illnesses and families. Three main findings emerged: First, the variability that arises due to systemic barriers in accessing and providing care; second, the exceptionally challenging workload that providers are facing as they work to address complex social care needs (housing, disability, food security), along with escalating palliative care needs; and, finally, the lack of structural support that providers and informal care networks receive. Conclusion: These findings will facilitate and build stronger person-centred/relationship-centred principles and practices between providers, patients, community, and informal care networks by highlighting the systemic barriers to accessing and providing person-centred care. Further, they will have important implications for future partnerships in integrated care delivery programs and initiatives, community policies, education programs, and provincial and national palliative care strategies.

Keywords: public health palliative care, palliative care nursing, care networks, informal care, integrated health care

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24328 Urban Greenery in the Greatest Polish Cities: Analysis of Spatial Concentration

Authors: Elżbieta Antczak

Abstract:

Cities offer important opportunities for economic development and for expanding access to basic services, including health care and education, for large numbers of people. Moreover, green areas (as an integral part of sustainable urban development) present a major opportunity for improving urban environments, quality of lives and livelihoods. This paper examines, using spatial concentration and spatial taxonomic measures, regional diversification of greenery in the cities of Poland. The analysis includes location quotients, Lorenz curve, Locational Gini Index, and the synthetic index of greenery and spatial statistics tools: (1) To verify the occurrence of strong concentration or dispersion of the phenomenon in time and space depending on the variable category, and, (2) To study if the level of greenery depends on the spatial autocorrelation. The data includes the greatest Polish cities, categories of the urban greenery (parks, lawns, street greenery, and green areas on housing estates, cemeteries, and forests) and the time span 2004-2015. According to the obtained estimations, most of cites in Poland are already taking measures to become greener. However, in the country there are still many barriers to well-balanced urban greenery development (e.g. uncontrolled urban sprawl, poor management as well as lack of spatial urban planning systems).

Keywords: greenery, urban areas, regional spatial diversification and concentration, spatial taxonomic measure

Procedia PDF Downloads 264
24327 Simulation and Hardware Implementation of Data Communication Between CAN Controllers for Automotive Applications

Authors: R. M. Kalayappan, N. Kathiravan

Abstract:

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

Procedia PDF Downloads 378
24326 A Qualitative Research of Online Fraud Decision-Making Process

Authors: Semire Yekta

Abstract:

Many online retailers set up manual review teams to overcome the limitations of automated online fraud detection systems. This study critically examines the strategies they adapt in their decision-making process to set apart fraudulent individuals from non-fraudulent online shoppers. The study uses a mix method research approach. 32 in-depth interviews have been conducted alongside with participant observation and auto-ethnography. The study found out that all steps of the decision-making process are significantly affected by a level of subjectivity, personal understandings of online fraud, preferences and judgments and not necessarily by objectively identifiable facts. Rather clearly knowing who the fraudulent individuals are, the team members have to predict whether they think the customer might be a fraudster. Common strategies used are relying on the classification and fraud scorings in the automated fraud detection systems, weighing up arguments for and against the customer and making a decision, using cancellation to test customers’ reaction and making use of personal experiences and “the sixth sense”. The interaction in the team also plays a significant role given that some decisions turn into a group discussion. While customer data represent the basis for the decision-making, fraud management teams frequently make use of Google search and Google Maps to find out additional information about the customer and verify whether the customer is the person they claim to be. While this, on the one hand, raises ethical concerns, on the other hand, Google Street View on the address and area of the customer puts customers living in less privileged housing and areas at a higher risk of being classified as fraudsters. Phone validation is used as a final measurement to make decisions for or against the customer when previous strategies and Google Search do not suffice. However, phone validation is also characterized by individuals’ subjectivity, personal views and judgment on customer’s reaction on the phone that results in a final classification as genuine or fraudulent.

Keywords: online fraud, data mining, manual review, social construction

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24325 Improving the Statistics Nature in Research Information System

Authors: Rajbir Cheema

Abstract:

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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24324 Data Mining Meets Educational Analysis: Opportunities and Challenges for Research

Authors: Carla Silva

Abstract:

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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24323 A Method of Detecting the Difference in Two States of Brain Using Statistical Analysis of EEG Raw Data

Authors: Digvijaysingh S. Bana, Kiran R. Trivedi

Abstract:

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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24322 A Study on Big Data Analytics, Applications and Challenges

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 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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24321 A Study on Big Data Analytics, Applications, and Challenges

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

Procedia PDF Downloads 76
24320 Improved K-Means Clustering Algorithm Using RHadoop with Combiner

Authors: Ji Eun Shin, Dong Hoon Lim

Abstract:

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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24319 Framework for Integrating Big Data and Thick Data: Understanding Customers Better

Authors: Nikita Valluri, Vatcharaporn Esichaikul

Abstract:

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

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24318 Incremental Learning of Independent Topic Analysis

Authors: Takahiro Nishigaki, Katsumi Nitta, Takashi Onoda

Abstract:

In this paper, we present a method of applying Independent Topic Analysis (ITA) to increasing the number of document data. The number of document data has been increasing since the spread of the Internet. ITA was presented as one method to analyze the document data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis (ICA). ICA is a technique in the signal processing; however, it is difficult to apply the ITA to increasing number of document data. Because ITA must use the all document data so temporal and spatial cost is very high. Therefore, we present Incremental ITA which extracts the independent topics from increasing number of document data. Incremental ITA is a method of updating the independent topics when the document data is added after extracted the independent topics from a just previous the data. In addition, Incremental ITA updates the independent topics when the document data is added. And we show the result applied Incremental ITA to benchmark datasets.

Keywords: text mining, topic extraction, independent, incremental, independent component analysis

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