Tesis sobre el tema "Big data concepts"

With the advent of cloud computing, resizable scalable infrastructures for data processing is now available to everyone. Software platforms and frameworks that support data intensive distributed applications such as Amazon Web Services and Apache Hadoop enable users to the necessary tools and infrastructure to work with thousands of scalable computers and process terabytes of data. However writing scalable applications that are run on top of these distributed frameworks is still a demanding and challenging task. The thesis aimed to advance the core scientific and technological means of managing, analyzing, visualizing, and extracting useful information from large data sets, collectively known as “big data”. The term “big-data” in this thesis refers to large, diverse, complex, longitudinal and/or distributed data sets generated from instruments, sensors, internet transactions, email, social networks, twitter streams, and/or all digital sources available today and in the future. We introduced architectures and concepts for implementing a cloud-based infrastructure for analyzing large volume of semi-structured and unstructured data. We built and evaluated an application prototype for collecting, organizing, processing, visualizing and analyzing data from the retail industry gathered from indoor navigation systems and social networks (Twitter, Facebook etc). Our finding was that developing large scale data analysis platform is often quite complex when there is an expectation that the processed data will grow continuously in future. The architecture varies depend on requirements. If we want to make a data warehouse and analyze the data afterwards (batch processing) the best choices will be Hadoop clusters and Pig or Hive. This architecture has been proven in Facebook and Yahoo for years. On the other hand, if the application involves real-time data analytics then the recommendation will be Hadoop clusters with Storm which has been successfully used in Twitter. After evaluating the developed prototype we introduced a new architecture which will be able to handle large scale batch and real-time data. We also proposed an upgrade of the existing prototype to handle real-time indoor navigation data.

La majorité des méthodes de classification de textes utilisent le paradigme du sac de mots pour représenter les textes. Pourtant cette technique pose différents problèmes sémantiques : certains mots sont polysémiques, d'autres peuvent être des synonymes et être malgré tout différenciés, d'autres encore sont liés sémantiquement sans que cela soit pris en compte et enfin, certains mots perdent leur sens s'ils sont extraits de leur groupe nominal. Pour pallier ces problèmes, certaines méthodes ne représentent plus les textes par des mots mais par des concepts extraits d'une ontologie de domaine, intégrant ainsi la notion de sens au modèle. Les modèles intégrant la représentation des textes par des concepts restent peu utilisés à cause des résultats peu satisfaisants. Afin d'améliorer les performances de ces modèles, plusieurs méthodes ont été proposées pour enrichir les caractéristiques des textes à l'aide de nouveaux concepts extraits de bases de connaissances. Mes travaux donnent suite à ces approches en proposant une étape d'enrichissement des modèles à l'aide d'une ontologie de domaine associée. J'ai proposé deux mesures permettant d'estimer l'appartenance aux catégories de ces nouveaux concepts. A l'aide de l'algorithme du classifieur naïf Bayésien, j'ai testé et comparé mes contributions sur le corpus de textes labéllisés Ohsumed et l'ontologie de domaine Disease Ontology. Les résultats satisfaisants m'ont amené à analyser plus précisément le rôle des relations sémantiques dans l'enrichissement des modèles. Ces nouveaux travaux ont été le sujet d'une seconde expérience où il est question d'évaluer les apports des relations hiérarchiques d'hyperonymie et d'hyponymie
Most of text-classification methods use the ``bag of words” paradigm to represent texts. However Bloahdom and Hortho have identified four limits to this representation: (1) some words are polysemics, (2) others can be synonyms and yet differentiated in the analysis, (3) some words are strongly semantically linked without being taken into account in the representation as such and (4) certain words lose their meaning if they are extracted from their nominal group. To overcome these problems, some methods no longer represent texts with words but with concepts extracted from a domain ontology (Bag of Concept), integrating the notion of meaning into the model. Models integrating the bag of concepts remain less used because of the unsatisfactory results, thus several methods have been proposed to enrich text features using new concepts extracted from knowledge bases. My work follows these approaches by proposing a model-enrichment step using a domain ontology, I proposed two measures to estimate to belong to the categories of these new concepts. Using the naive Bayes classifier algorithm, I tested and compared my contributions on the Ohsumed corpus using the domain ontology ``Disease Ontology”. The satisfactory results led me to analyse more precisely the role of semantic relations in the enrichment step. These new works have been the subject of a second experiment in which we evaluate the contributions of the hierarchical relations of hypernymy and hyponymy

Due to urbanization and the population of cities producing up to 75% of emission, Smart City concepts, looking at sustainability and more efficiency within the city, with the help of IoT and ICT based technology, are seen as an opportunity to act future-oriented, today. Construction and transportation are seen as the main contributors on the way of change from energy consumption to energy production. Enhancing infrastructure to improve the quality of all sorts of public transportation is thus of utter importance to governance, interested in Smart City concepts. Looking at the literature, waterborne transportation has not received much scientific attention in the context of being implemented into Smart City initiatives. This systematic literature research draws logical conclusions from the researched literature. The research concludes with a research agenda for future research to deepen the knowledge in the explanatory field of waterborne transportation making use of Smart City technologies. The main findings of this thesis are: First, waterborne transportation poses a threat to the environment and impacts sustainability of water bodies, as well as the environment surrounding them. Second, Smart City technologies can successfully be implemented in waterborne transportation when carefully planned. Barriers for the implementation of Smart City concepts can be lack of knowledge, investment, data security and readiness of infrastructure. These can be overcome through the help of collaboration and knowledge sharing among the involved stakeholders. Third, the image of the industry can be shifted, as well as its direct impact and the indirect use of waterborne transportation can be made more sustainable and ecosystem friendly. This transition attracts further customers, who otherwise were not willing to use waterborne transportation. In order to make waterborne transportation more sustainable and part of the Smart City movement, knowledge needs to be deepened and awareness about the topic needs to be spread. Its use of Smart City technologies needs to be further investigated, looking at specific types and tailored solutions for them, as well as how beneficial such an investment can be for governments and companies regarding ecological costs and their image. This thesis mainly aims to help scholars, interested in further research to deepen the knowledge on waterborne transportation in a sustainability context, but also companies and governance, looking to make waterborne transportation more sustainable.

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The world faces exponential data growth. Data is created by smart devices, RFID technologies (Radio-Frequency IDentification), sensors, social networks, video surveillance and more. These generated data are no longer considered static, whose usefulness ends after the purpose of the collection is reached, they have become the raw material of the business, a vital economic resource, used to create a new form of economic value. Then comes the concept of “big data”. The objective of this research is to raise the discussion about the concept of big data, drawing from the current literature definitions that offer subsidies for the understanding of its real meaning and impact in the generation of useful ideas and goods and services of significant value. However, because it is a recent theme, the available literature is scarce. It is an applied purpose research with a descriptive purpose and uses the qualitative method of approach. It has by type of research the review of the literature for the theoretical basis, and also the study review of two cases through an exploratory research to collect the data to be analyzed. It seeks to confront the theory with the identified hypotheses and practices, to assess its adherence, arriving at informed conclusions, and to suggest future studies that may continue this line
O mundo enfrenta um crescimento exponencial de dados. Dados são criados por dispositivos inteligentes, tecnologias RFID (Radio-Frequency IDentification), sensores, redes sociais, vigilância por vídeo e muito mais. Esses dados gerados não são mais considerados estáticos, cuja utilidade termina depois que o objetivo da coleta é alcançado, eles se tornaram a matéria-prima dos negócios, um recurso econômico vital, usado para criar uma nova forma de valor econômico. Surge então o conceito de “big data”. O objetivo desta pesquisa é levantar a discussão sobre o conceito de big data, extraindo da literatura atual definições que ofereçam subsídios para o entendimento de seu real significado e impacto na geração de ideias úteis e bens e serviços de valor significativo. Entretanto, por ser um tema recente, a literatura disponível é escassa. É uma investigação de finalidade aplicada, com um objetivo descritivo e utiliza o método qualitativo de abordagem. Tem por tipo de pesquisa a revisão da literatura para a fundamentação teórica, e também a revisão de estudo de dois casos através de pesquisa exploratória para a coleta dos dados a serem analisados. Busca confrontar a teoria com as hipóteses e práticas identificadas, para avaliar sua aderência, chegando em conclusões fundamentadas, além de sugerir estudos futuros que podem dar continuidade a esta linha abordada

This work explores an application concept for small mobile devices, displaying structured "Big Data" based on human web reporting. The target user is a traveler interested in geopolitical events in the visited region and the concept focuses on high level signals to describethe situation and allows for following up, down to original reporting sources. Interviews and a survey was used to investigate the target user group's current behavior and needs while traveling and in unstable regions. The design process is described in reference to interaction design practices and successful applications on the market today, resulting in aconcept presented in the form of high fidelity sketches, well documented interaction style and transitions, and a clickable low delity prototype. The work can be used as a reference document for further development.

L'ère du Big Data a révolutionné la manière dont les données sont créées et traitées. Dans ce contexte, de nombreux défis se posent, compte tenu de la quantité énorme de données disponibles qui doivent être efficacement gérées et traitées afin d’extraire des connaissances. Cette thèse explore la symbiose de l'apprentissage en mode batch et en flux, traditionnellement considérés dans la littérature comme antagonistes, sur le problème de la classification à partir de flux de données en évolution. L'apprentissage en mode batch est une approche bien établie basée sur une séquence finie: d'abord les données sont collectées, puis les modèles prédictifs sont créés, finalement le modèle est appliqué. Par contre, l’apprentissage par flux considère les données comme infinies, rendant le problème d’apprentissage comme une tâche continue (sans fin). De plus, les flux de données peuvent évoluer dans le temps, ce qui signifie que la relation entre les caractéristiques et la réponse correspondante peut changer. Nous proposons un cadre systématique pour prévoir le surendettement, un problème du monde réel ayant des implications importantes dans la société moderne. Les deux versions du mécanisme d'alerte précoce (batch et flux) surpassent les performances de base de la solution mise en œuvre par le Groupe BPCE, la deuxième institution bancaire en France. De plus, nous introduisons une méthode d'imputation évolutive basée sur un modèle pour les données manquantes dans la classification. Cette méthode présente le problème d'imputation sous la forme d'un ensemble de tâches de classification / régression résolues progressivement.Nous présentons un cadre unifié qui sert de plate-forme d'apprentissage commune où les méthodes de traitement par batch et par flux peuvent interagir de manière positive. Nous montrons que les méthodes batch peuvent être efficacement formées sur le réglage du flux dans des conditions spécifiques. Nous proposons également une adaptation de l'Extreme Gradient Boosting algorithme aux flux de données en évolution. La méthode adaptative proposée génère et met à jour l'ensemble de manière incrémentielle à l'aide de mini-lots de données. Enfin, nous présentons scikit-multiflow, un framework open source en Python qui comble le vide en Python pour une plate-forme de développement/recherche pour l'apprentissage à partir de flux de données en évolution
The Big Data era has revolutionized the way in which data is created and processed. In this context, multiple challenges arise given the massive amount of data that needs to be efficiently handled and processed in order to extract knowledge. This thesis explores the symbiosis of batch and stream learning, which are traditionally considered in the literature as antagonists. We focus on the problem of classification from evolving data streams.Batch learning is a well-established approach in machine learning based on a finite sequence: first data is collected, then predictive models are created, then the model is applied. On the other hand, stream learning considers data as infinite, rendering the learning problem as a continuous (never-ending) task. Furthermore, data streams can evolve over time, meaning that the relationship between features and the corresponding response (class in classification) can change.We propose a systematic framework to predict over-indebtedness, a real-world problem with significant implications in modern society. The two versions of the early warning mechanism (batch and stream) outperform the baseline performance of the solution implemented by the Groupe BPCE, the second largest banking institution in France. Additionally, we introduce a scalable model-based imputation method for missing data in classification. This method casts the imputation problem as a set of classification/regression tasks which are solved incrementally.We present a unified framework that serves as a common learning platform where batch and stream methods can positively interact. We show that batch methods can be efficiently trained on the stream setting under specific conditions. The proposed hybrid solution works under the positive interactions between batch and stream methods. We also propose an adaptation of the Extreme Gradient Boosting (XGBoost) algorithm for evolving data streams. The proposed adaptive method generates and updates the ensemble incrementally using mini-batches of data. Finally, we introduce scikit-multiflow, an open source framework in Python that fills the gap in Python for a development/research platform for learning from evolving data streams

This bachelor’s thesis is a literature review about big data, privacy concerns for individual consumers and how these two overlap together in a way to possibly explain what are the privacy concerns that customers have, and the companies maybe should think about, when they utilize data as a marketing tool. The thesis introduces an integrative framework for the privacy concerns emerging from the process of big data management for marketing purposes and gives insights for phases in this process first separately from companies point of view, then from the consumers point of view regarding the privacy issues and then together by bringing these two issues together.

Feature extraction is an extremely important pre-processing step to pattern recognition, and machine learning problems. This thesis highlights how one can best extract features from the data in an exhaustively online and purely adaptive manner. The solution to this problem is given for both labeled and unlabeled datasets, by presenting a number of novel on-line learning approaches. Specifically, the differential equation method for solving the generalized eigenvalue problem is used to derive a number of novel machine learning and feature extraction algorithms. The incremental eigen-solution method is used to derive a novel incremental extension of linear discriminant analysis (LDA). Further the proposed incremental version is combined with extreme learning machine (ELM) in which the ELM is used as a preprocessor before learning. In this first key contribution, the dynamic random expansion characteristic of ELM is combined with the proposed incremental LDA technique, and shown to offer a significant improvement in maximizing the discrimination between points in two different classes, while minimizing the distance within each class, in comparison with other standard state-of-the-art incremental and batch techniques. In the second contribution, the differential equation method for solving the generalized eigenvalue problem is used to derive a novel state-of-the-art purely incremental version of slow feature analysis (SLA) algorithm, termed the generalized eigenvalue based slow feature analysis (GENEIGSFA) technique. Further the time series expansion of echo state network (ESN) and radial basis functions (EBF) are used as a pre-processor before learning. In addition, the higher order derivatives are used as a smoothing constraint in the output signal. Finally, an online extension of the generalized eigenvalue problem, derived from James Stone’s criterion, is tested, evaluated and compared with the standard batch version of the slow feature analysis technique, to demonstrate its comparative effectiveness. In the third contribution, light-weight extensions of the statistical technique known as canonical correlation analysis (CCA) for both twinned and multiple data streams, are derived by using the same existing method of solving the generalized eigenvalue problem. Further the proposed method is enhanced by maximizing the covariance between data streams while simultaneously maximizing the rate of change of variances within each data stream. A recurrent set of connections used by ESN are used as a pre-processor between the inputs and the canonical projections in order to capture shared temporal information in two or more data streams. A solution to the problem of identifying a low dimensional manifold on a high dimensional dataspace is then presented in an incremental and adaptive manner. Finally, an online locally optimized extension of Laplacian Eigenmaps is derived termed the generalized incremental laplacian eigenmaps technique (GENILE). Apart from exploiting the benefit of the incremental nature of the proposed manifold based dimensionality reduction technique, most of the time the projections produced by this method are shown to produce a better classification accuracy in comparison with standard batch versions of these techniques - on both artificial and real datasets.

Through the use of bio-metric data and audio recordings this research explores the body as it engages the concert environment. With the help of wearable technology and digital audio recording devices, data from four concerts was documented. Additionally personal reflections were recorded at the conclusion of each concert. These documents serve as qualitative data and a point of comparison between the quantitative recordings. These records were then used in the construction of an interactive data visualization that allows further exploration of the data collected by means of a visual interface.
Master of Fine Arts

Background Big Data Analytics take place in almost every sector of new business world. Nowadays, banks are also adopting Big Data to handle the huge number of data that generate every day. Big Data helps banks to provide a fast, personalised service in a cost efficient way. On the other hand, Big Data has some privacy issues as it deals with a lot of data that can be decoded by third party. It is also the case in online banking as it is involved with personal and financial information. Privacy concerns also vary among different cultures. PurposeThe purpose of this cross-cultural study is to investigate online privacy concerns in the context of Big Data MethodologyA quantitative approach has been followed and data were collected through an online survey to understand the relations between variables. ConclusionThe findings indicate that the relationship between the privacy concern and its antecedents differ between France and Bangladesh. Though for both countries, the desire upon transparency showed a significant positive relationship with online privacy concerns. Additionally, for both countries a high privacy concern will not conduct to lower consumer trust and consumer engagement in online baking. The findings involving moderator variables were not significant at al

Based on the description and definition of technology and processes falling within the vision of the fourth industrial revolution with the aim of creating intelligent factories, this diploma thesis deals with the principles of the Industry 4.0 concept in Hilti's production plant with a focus on transport and supply of production equipment. The aim of the work is to create a comprehensive proposal that takes into account all the necessary aspects associated with upgrading the existing state of inter-operational transport in a particular production line to fully automated, flexible and autonomous transport of materials and products in the context of Industry 4.0. A prerequisite for creating a design is the connection of automatically guided vehicles (AGVs) serving individual transport orders. The selection of the vehicle was made taking into account the safety of movement, the method of charging, the system and network integrity of existing and proposed technologies and components. The intention is not only to automate the inter-operational service, but also on the basis of the created automation concept, the ability to autonomously procure the flow of material and products. The mathematical calculation of capacity planning in the production line helped to determine the total load and the number of vehicles needed for continuous procurement of transport requirements. The result of the design part is also the design of specific transport routes and transport conditions that AGV vehicles must comply with in order to maintain a high level of safety. Transparency and a constant overview of transported products is provided by the presented scheme for identification of production batches, Auto-ID system. The financial efficiency of the whole project elaborated in the diploma thesis is evaluated as payable after 4 years from the implementation of the proposal. The financial efficiency of the whole project elaborated in the diploma thesis is evaluated as payable after 4 years from the implementation of the proposal due to high labor costs.

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação
Com o aparecimento do termo Big Data, muitos temas surgem neste contexto e o Data Warehousing é um deles. A forma de desenvolvimento de um Data Warehouse tradicional, e as tecnologias que são utilizadas para o efeito, podem não suportar as grandes quantidades de dados que são geradas nos dias de hoje pelas organizações. As organizações precisam de mais informação e com mais qualidade, de forma a desenvolverem os seus processos de trabalho para terem mais êxito no mercado, que cada vez é mais exigente. Assim, a principal finalidade deste trabalho é propor uma arquitetura para Data Warehousing em contexto Big Data, utilizando um modelo de dados no Hive e um Data Warehouse tradicional como fonte de dados. Como as organizações tem os seus Data Warehouses com muitos dados e essas ferramentas já tem dificuldade em processar esses mesmos dados, este trabalho também pretende propor uma forma de migrar os dados de um Data Warehouse tradicional para um Data Warehouse em contexto de Big Data. Neste trabalho, foi elaborado um enquadramento conceptual de Big Data e Data Warehousing, incluindo termos e conceitos associados a estes, as suas características, processamento de dados, NoSQL, bases de dados In-Memory, entre outros. Além disso, foi realizada uma análise de arquiteturas de Data Warehousing em contexto Big Data já existentes, para perceber o que já existe em relação a este tema. É também apresentado um enquadramento tecnológico, com vista a descrever algumas das tecnologias que têm um papel relevante na proposta da arquitetura, com especial atenção para o ecossistema do Hadoop, e os componentes Hive e Impala. Após a realização do estado da arte e retiradas algumas conclusões, propõe-se uma arquitetura que permite de uma forma flexível construir um Data Warehouse em contexto Big Data, onde a arquitetura é constituída por um conjunto de fluxos de dados e componentes tecnológicos. Antes de desenvolver a arquitetura, foram realizados testes aos tempos de processamento do Hive e do Impala, para perceber como estas tecnologias se poderiam integrar, com o Hive a desempenhar o papel de Data Warehouse e o Impala com o papel de motor de pesquisas para a análise e visualização dos dados. Depois da proposta da arquitetura, foi realizado um trabalho de experimentação que fez uso do ecossistema do Hadoop e do Talend para implementar a arquitetura. A arquitetura foi implementada e validada com sucesso em todos os níveis, desde os componentes escolhidos, os tempos de processamento, a implementação dos fluxos de ETL/ELT e do modelo de dados utilizado no Hive.
With the emergence of the Big Data term, many issues arise in this context and Data Warehousing is one of those issues. The way traditional Data Warehouses and technologies are used for this purpose may not support the large amounts of data that are generated by today organizations. Organizations need more information and better quality in that information in order to develop their work processes and be more successful in the market, which is increasingly demanding. The main purpose of this work is to propose an architecture for Data Warehousing in Big Data contexts with Hive as the Data Warehouse repository and a traditional Data Warehouse as data source. As organizations have their data warehouses with lots of data and these tools already have difficulty processing such data, this paper also aims to propose a way to migrate data from a traditional Data Warehouse for a Data Warehouse in the context of Big Data. In this work, a literature review of Big Data and Data Warehousing was developed, including its characteristics and concepts such as, data processing, NoSQL, and In-Memory databases, among others. In addition, an analysis of data models for Data Warehousing in Big Data was performed, considering several available approaches. A technological overview is also presented, in order to describe some of the technologies that may play an important role in the design and validation of the proposed architecture, giving this work special attention to the ecosystem of Hadoop, and the Impala and Hive components. After the completion and analysis of the state of the art, it is proposed an architecture that provides a general overview of the way to build a Data Warehouse in the context Big Data, where the architecture is composed of a set of data flows and technology components. Before implementing the architecture, a benchmark was conducted to verify the processing times of Hive and Impala, an important step to understand how these technologies could be integrated and fit into the architecture, where Hive plays the role of a Data Warehouse and Impala is the driving force for the analysis and visualization of data. After the proposal of the architecture, it was implemented using tools like the Hadoop ecosystem and Talend. The architecture was succesfully implemented and validated at all levels, from the architecture itself to the chosen components, processing times, implementation of the flows ETL / ELT and data models used in Hive.

碩士
大葉大學
電機工程學系
104
The thesis focuses on applying the Radio Frequency Identification (RFID) or Near Field Communication (NFC), which have the features of real-time access and Cloud computing, to transfer the Big Data stored in the Web space. The Web address of the file space is passed to a mobile device, in the meantime, the Big Data is transmitted to a smart device Via the Blue-tooth protocol. Moreover, the gathered data stored in the Web space will be statistically evaluated in the mobile device. Finally, the calculated data will be checked in both sides include local device and Web device. There is a diversity and multi-variates data evaluation system is implemented in this study. Furthermore, by means of continuous reading and displaying functions, the sensing devices of RFID and NFC are applied in the functions of access statistical analysis. All the gathered data is obtained from the tag embedded in a RFID reader，then Combined with the so-called Big Data. The Visual Basic is adopted as the programming language for planning to be as the position, data size, and the relevant device setting. At last, there are some of the user interface (UI) are completed for demonstration. Certainly, the coding is programmed by Visual Basic for testing, debugging and program execution.

碩士
國立中山大學
資訊管理學系研究所
105
The coming of the generation of big data makes the issues of information privacy receive more attention. In the past, most of research on big data focused on programming or the privacy concerns of the information which users are willingly to give. Few studies focus on the privacy concerns of big data analysis and prediction or the users’ behavior in big data environment. The users’ behavior may lead the result of big data analysis to inaccurate, and the doubt of privacy also resulting in poor user experience. Based on prior research, we identified four features of big data privacy risks, and also the constructs of information privacy concerns. In this research we examined the impact of these constructs on trust belief and risk belief, and also explored the cause of user disguises. Data collected from 570 users of the Internet provide enough reliability and validity for the research model. The results suggested that information privacy concerns enhance risk belief and decrease trust belief. Most of the big data privacy risks features lead to risk belief. Risk belief indeed foster more user disguises, but the trust belief is not an important determinant of risk belief and user disguises. Practical implications for theory and practice and suggestions for future research are also discussed.

碩士
國立中興大學
法律學系科技法律碩士班
104
The collection volume, variety and velocity of data make Big Data different from the traditional methods, which often offers unexpected analysis and results. However, when we enjoy the benefits of Big Data, it also creates many new concerns on privacy protections. This thesis starts from the comparative legal concept of Privacy between the U.S.A. and Taiwan and discusses whether the application of Big Data falls in the range of current Right to Privacy. Second, we analysised the US and Taiwan cases which shares similar characteristic of Big Data, such as metadata and GPS, to observe whether the rules change the expectation of privacy among people in the U.S.A. and Taiwan respectively. Then finally prove that Big Data indeed has the possibility to infringe the Right to Privacy. From the view of implementation on privacy protection, this thesis reviewed the U.S.A. Fair Information Practices Principles based on the past technology to discuss whether Big Data causes any impact on the basic principles in privacy protection, including de-identification, limitation on collection and usage, purpose specification, and erodes the structure of traditional Privacy Law. Also, reconsidering the influence of Big Data usage makes the definition of personal information become much more ambiguous. If the agency over expanded the protection scope of the Privacy Law, those non-personal information that usually been used by Big Data might not be regulated by Personal Information Protection Act and caused the threatened to the protection of privacy.