Search results for: sparsely representable data
24606 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 6124605 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 35724604 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
Procedia PDF Downloads 8024603 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 34924602 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 35224601 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
Procedia PDF Downloads 47324600 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
Procedia PDF Downloads 9424599 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
Procedia PDF Downloads 37824598 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 29124597 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 8224596 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 39624595 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
Procedia PDF Downloads 15124594 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
Procedia PDF Downloads 35224593 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
Procedia PDF Downloads 45724592 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
Procedia PDF Downloads 7724591 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 8624590 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
Procedia PDF Downloads 42624589 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
Procedia PDF Downloads 15224588 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
Procedia PDF Downloads 30324587 Open Data for e-Governance: Case Study of Bangladesh
Authors: Sami Kabir, Sadek Hossain Khoka
Abstract:
Open Government Data (OGD) refers to all data produced by government which are accessible in reusable way by common people with access to Internet and at free of cost. In line with “Digital Bangladesh” vision of Bangladesh government, the concept of open data has been gaining momentum in the country. Opening all government data in digital and customizable format from single platform can enhance e-governance which will make government more transparent to the people. This paper presents a well-in-progress case study on OGD portal by Bangladesh Government in order to link decentralized data. The initiative is intended to facilitate e-service towards citizens through this one-stop web portal. The paper further discusses ways of collecting data in digital format from relevant agencies with a view to making it publicly available through this single point of access. Further, possible layout of this web portal is presented.Keywords: e-governance, one-stop web portal, open government data, reusable data, web of data
Procedia PDF Downloads 34624586 Resource Framework Descriptors for Interestingness in Data
Authors: C. B. Abhilash, Kavi Mahesh
Abstract:
Human beings are the most advanced species on earth; it's all because of the ability to communicate and share information via human language. In today's world, a huge amount of data is available on the web in text format. This has also resulted in the generation of big data in structured and unstructured formats. In general, the data is in the textual form, which is highly unstructured. To get insights and actionable content from this data, we need to incorporate the concepts of text mining and natural language processing. In our study, we mainly focus on Interesting data through which interesting facts are generated for the knowledge base. The approach is to derive the analytics from the text via the application of natural language processing. Using semantic web Resource framework descriptors (RDF), we generate the triple from the given data and derive the interesting patterns. The methodology also illustrates data integration using the RDF for reliable, interesting patterns.Keywords: RDF, interestingness, knowledge base, semantic data
Procedia PDF Downloads 15424585 Data Mining Practices: Practical Studies on the Telecommunication Companies in Jordan
Authors: Dina Ahmad Alkhodary
Abstract:
This study aimed to investigate the practices of Data Mining on the telecommunication companies in Jordan, from the viewpoint of the respondents. In order to achieve the goal of the study, and test the validity of hypotheses, the researcher has designed a questionnaire to collect data from managers and staff members from main department in the researched companies. The results shows improvements stages of the telecommunications companies towered Data Mining.Keywords: data, mining, development, business
Procedia PDF Downloads 48924584 The Impact of System and Data Quality on Organizational Success in the Kingdom of Bahrain
Authors: Amal M. Alrayes
Abstract:
Data and system quality play a central role in organizational success, and the quality of any existing information system has a major influence on the effectiveness of overall system performance.Given the importance of system and data quality to an organization, it is relevant to highlight their importance on organizational performance in the Kingdom of Bahrain. This research aims to discover whether system quality and data quality are related, and to study the impact of system and data quality on organizational success. A theoretical model based on previous research is used to show the relationship between data and system quality, and organizational impact. We hypothesize, first, that system quality is positively associated with organizational impact, secondly that system quality is positively associated with data quality, and finally that data quality is positively associated with organizational impact. A questionnaire was conducted among public and private organizations in the Kingdom of Bahrain. The results show that there is a strong association between data and system quality, that affects organizational success.Keywords: data quality, performance, system quality, Kingdom of Bahrain
Procedia PDF Downloads 48524583 Cloud Computing in Data Mining: A Technical Survey
Authors: Ghaemi Reza, Abdollahi Hamid, Dashti Elham
Abstract:
Cloud computing poses a diversity of challenges in data mining operation arising out of the dynamic structure of data distribution as against the use of typical database scenarios in conventional architecture. Due to immense number of users seeking data on daily basis, there is a serious security concerns to cloud providers as well as data providers who put their data on the cloud computing environment. Big data analytics use compute intensive data mining algorithms (Hidden markov, MapReduce parallel programming, Mahot Project, Hadoop distributed file system, K-Means and KMediod, Apriori) that require efficient high performance processors to produce timely results. Data mining algorithms to solve or optimize the model parameters. The challenges that operation has to encounter is the successful transactions to be established with the existing virtual machine environment and the databases to be kept under the control. Several factors have led to the distributed data mining from normal or centralized mining. The approach is as a SaaS which uses multi-agent systems for implementing the different tasks of system. There are still some problems of data mining based on cloud computing, including design and selection of data mining algorithms.Keywords: cloud computing, data mining, computing models, cloud services
Procedia PDF Downloads 47224582 Cross-border Data Transfers to and from South Africa
Authors: Amy Gooden, Meshandren Naidoo
Abstract:
Genetic research and transfers of big data are not confined to a particular jurisdiction, but there is a lack of clarity regarding the legal requirements for importing and exporting such data. Using direct-to-consumer genetic testing (DTC-GT) as an example, this research assesses the status of data sharing into and out of South Africa (SA). While SA laws cover the sending of genetic data out of SA, prohibiting such transfer unless a legal ground exists, the position where genetic data comes into the country depends on the laws of the country from where it is sent – making the legal position less clear.Keywords: cross-border, data, genetic testing, law, regulation, research, sharing, South Africa
Procedia PDF Downloads 12224581 The Study of Security Techniques on Information System for Decision Making
Authors: Tejinder Singh
Abstract:
Information system is the flow of data from different levels to different directions for decision making and data operations in information system (IS). Data can be violated by different manner like manual or technical errors, data tampering or loss of integrity. Security system called firewall of IS is effected by such type of violations. The flow of data among various levels of Information System is done by networking system. The flow of data on network is in form of packets or frames. To protect these packets from unauthorized access, virus attacks, and to maintain the integrity level, network security is an important factor. To protect the data to get pirated, various security techniques are used. This paper represents the various security techniques and signifies different harmful attacks with the help of detailed data analysis. This paper will be beneficial for the organizations to make the system more secure, effective, and beneficial for future decisions making.Keywords: information systems, data integrity, TCP/IP network, vulnerability, decision, data
Procedia PDF Downloads 30124580 Data Integration with Geographic Information System Tools for Rural Environmental Monitoring
Authors: Tamas Jancso, Andrea Podor, Eva Nagyne Hajnal, Peter Udvardy, Gabor Nagy, Attila Varga, Meng Qingyan
Abstract:
The paper deals with the conditions and circumstances of integration of remotely sensed data for rural environmental monitoring purposes. The main task is to make decisions during the integration process when we have data sources with different resolution, location, spectral channels, and dimension. In order to have exact knowledge about the integration and data fusion possibilities, it is necessary to know the properties (metadata) that characterize the data. The paper explains the joining of these data sources using their attribute data through a sample project. The resulted product will be used for rural environmental analysis.Keywords: remote sensing, GIS, metadata, integration, environmental analysis
Procedia PDF Downloads 11224579 Analysis of Genomics Big Data in Cloud Computing Using Fuzzy Logic
Authors: Mohammad Vahed, Ana Sadeghitohidi, Majid Vahed, Hiroki Takahashi
Abstract:
In the genomics field, the huge amounts of data have produced by the next-generation sequencers (NGS). Data volumes are very rapidly growing, as it is postulated that more than one billion bases will be produced per year in 2020. The growth rate of produced data is much faster than Moore's law in computer technology. This makes it more difficult to deal with genomics data, such as storing data, searching information, and finding the hidden information. It is required to develop the analysis platform for genomics big data. Cloud computing newly developed enables us to deal with big data more efficiently. Hadoop is one of the frameworks distributed computing and relies upon the core of a Big Data as a Service (BDaaS). Although many services have adopted this technology, e.g. amazon, there are a few applications in the biology field. Here, we propose a new algorithm to more efficiently deal with the genomics big data, e.g. sequencing data. Our algorithm consists of two parts: First is that BDaaS is applied for handling the data more efficiently. Second is that the hybrid method of MapReduce and Fuzzy logic is applied for data processing. This step can be parallelized in implementation. Our algorithm has great potential in computational analysis of genomics big data, e.g. de novo genome assembly and sequence similarity search. We will discuss our algorithm and its feasibility.Keywords: big data, fuzzy logic, MapReduce, Hadoop, cloud computing
Procedia PDF Downloads 29424578 Forthcoming Big Data on Smart Buildings and Cities: An Experimental Study on Correlations among Urban Data
Authors: Yu-Mi Song, Sung-Ah Kim, Dongyoun Shin
Abstract:
Cities are complex systems of diverse and inter-tangled activities. These activities and their complex interrelationships create diverse urban phenomena. And such urban phenomena have considerable influences on the lives of citizens. This research aimed to develop a method to reveal the causes and effects among diverse urban elements in order to enable better understanding of urban activities and, therefrom, to make better urban planning strategies. Specifically, this study was conducted to solve a data-recommendation problem found on a Korean public data homepage. First, a correlation analysis was conducted to find the correlations among random urban data. Then, based on the results of that correlation analysis, the weighted data network of each urban data was provided to people. It is expected that the weights of urban data thereby obtained will provide us with insights into cities and show us how diverse urban activities influence each other and induce feedback.Keywords: big data, machine learning, ontology model, urban data model
Procedia PDF Downloads 41024577 Data-driven Decision-Making in Digital Entrepreneurship
Authors: Abeba Nigussie Turi, Xiangming Samuel Li
Abstract:
Data-driven business models are more typical for established businesses than early-stage startups that strive to penetrate a market. This paper provided an extensive discussion on the principles of data analytics for early-stage digital entrepreneurial businesses. Here, we developed data-driven decision-making (DDDM) framework that applies to startups prone to multifaceted barriers in the form of poor data access, technical and financial constraints, to state some. The startup DDDM framework proposed in this paper is novel in its form encompassing startup data analytics enablers and metrics aligning with startups' business models ranging from customer-centric product development to servitization which is the future of modern digital entrepreneurship.Keywords: startup data analytics, data-driven decision-making, data acquisition, data generation, digital entrepreneurship
Procedia PDF Downloads 316