Bigdata

Introduzione: l'intelligenza artificiale E' il 1956. Al Darmouth College, nel New Hampshire Alan Touring, padre dell'informatica moderna, ed altri importanti figure dell'epoca gettano le basi per quella che sarebbe stata poi l'Intelligenza artificiale (A.I.). Questa consiste "in un insieme eterogeneo di tecniche e metodi volti a costruire sistemi artificiali dotati di capacità cognitive, che siano quindi capaci di riconoscere, classificare, ragionare, diagnosticare e anche agire, o che siano dotati almeno di alcune di queste proprietà" (Treccani). Oggi l'intelligenza artificiale , gioca ormai un ruolo fondamentale per lo sviluppo di stati e imprese. Per esempio,il ministro dell'innovazione tecnologica e della transizione digitale , Vittorio Colao punta , tra le altre cose, ad automatizzare parte delle procedure e decisioni delle pubblica amministrazione facendo uso di algoritmi che apprendono e "pensano" autonomamente (c.d. machine learning).

Unstructured data, which is the heart of big data, are generating in every moment due to the revolution of Internet. Relational data model is not the right tool to handle such unstructured data because it has limitation of scalability, accessibility, flexibility, agility and in addition redundancy in database. In order to resolve those flaws of relational data model, a number of non-relational data models have been invented. Among those data models, this paper focuses on document oriented data model on some cosmetic products, explores the different shortcomings of relational data model compared to non-relational data model and suggests the best data model for cosmetic product.

Cet article étudie une forme de complémentaire santé proposant des incitations financières pour favoriser l’adoption ou le renforcement de comportements « sains ». Le fonctionnement du produit « Vitality » commercialisé en France depuis 2017 est dans un premier temps décrit, ainsi que ses déclinaisons européennes et internationales. Une analyse approfondie permet, ensuite, de localiser l’origine de ces « programmes de bien-être » aux États-Unis, en en rapportant les limites spécifiques. Les arguments des défenseurs comme ceux des critiques de l’assurance santé comportementales sont, enfin, mis en regard des enjeux de la protection sociale.

در عصر فناوری ‌اطلاعات و ارتباطات، نگرش مصرف‌کنندگان به ادراک از خلاقیت پیامک‌های تبلیغاتی، یکی از موضوعات مطرح در حوزه بازاریابی است و هنوز شناخت چندانی درباره آن وجود ندارد. هدف این پژوهش، بررسی تأثیر ادراک از خلاقیت پیامک‌های تبلیغاتی بر نگرش و واکنش مصرف‌کنندگان به پیامک‌های تبلیغاتی ارسالی به تلفن همراه آنهاست. جامعه آماری این تحقیق کاربران تلفن همراه در شهر تهران و حجم نمونه ۳۸۵ نفر است که به‌صورت نمونه‌گیری دردسترس انتخاب شدند. تحقیق حاضر از نظر هدف، کاربردی و از نظر نحوه گردآوری‌ داده‌ها توصیفی- پیمایشی است. مدل مورد مطالعه با استفاده از مدل‌سازی معادلات ساختاری و رویکرد کمترین مربعات جزئی آزمون شد. نتایج بیانگر آن است که ادراک از خلاقیت پیامک تبلیغاتی بر نگرش و واکنش‌رفتاری مصرف‌کنندگان به پیامک‌های تبلیغاتی ارسالی به تلفن همراه آنان تأثیر مثبت معناداری دارد، درحالی‌که ادراک از خلاقیت پیامک تبلیغاتی به‌صورت غیرمستقیم و از طریق متغیر میانجی نگرش نیز بر واکنش ‌رفتاری مصرف‌کنندگان تأثیر می‌گذارد.

The ontology is a common place where we can get the shared knowledge about the common concepts. In medical domains, university data bases and in the study of botanical areas of research we can see the applications of ontologies. The main objective of our research is to propose a job recommendation system based on the ontology. The process here is construction of the ontology from various job portals and notifying the result set to both job seekers and employers. The important attempt that here we are describing is to adapt the dynamism to the ontology with the idea of Slowly Changing Source detection based on lookup and update strategy. Look up will identify the change happens or not, whereas the update strategy tracks the change in terms of updating, deletion or insertion of the source. The benefit of our work is dynamic data management provision to the ontology constructed. We believe that our method will give the best results in case of accuracy and time parameters in the job notifications.

Different databases are designed to solve different problems. Using a single database engine for all of the requirements usually leads to non- performing solutions. So in this paper, the term polyglot persistence has been introduced to describe using different data storage technologies to handle varying data storage needs

With rapid digitalization, organizations are producing a lot of data as part of their day-today operations. These data are stored either on their legacy platforms or in any cloud storage. The maximum volume of these data stored in the cloud is unstructured, and much-hidden information is kept inside those. Our objective is to read those unstructured data elements like pdf or text files, audio, or video files to extract meaning from what can be used for the enterprises. Utilizing the cloud-based big data platforms, we use spark engines to read these unstructured data to securely store and transform them to a structured format, which will be our inputs to the natural language processing (NLP).

Today, service organizations are shifting their focus from " transactional exchange " to " relational exchange " for developing mutually satisfying relationship with customers. Extended relationships are reported to have a significant impact on transaction cost and profitability, and customer lifetime value. Serving the customers, in true sense, is the need of the hour as the customer was, is and will remain the central focus of all organizational activities. The hotel industry, especially the luxury segment hotels needs to be purely customer-centric and focus on the customer needs and duly fulfill them.

Explosion of biological data due to large-scale genomic research and advances in high throughput data generation tools result in massive distributed datasets. Analysis of such large non-relational, heterogeneous, and distributed datasets is emerging challenge in data driven biomedical industries. Highly complex biological data require unconventional computational approaches and knowledge-based solutions. Distributed datasets need to be reduced to smaller datasets that can be efficiently queried. Since genomic and biological data is generated in large volume and is stored in geographically diverse locations, distributed computing on multiple clusters, our objective here is to assess the feasibility of using Cloud based platform to analyze genomic big data. In this paper we present an enhanced Hadoop architecture to reduce computation by utilizing "Common Features" before performing redundant computation. The enhanced Hadoop architecture allows the jobs to share these common features among them. The common features describe the contents of data in blocks and can be used to determine DataNodes that store the required data.

Facebook merupakan perusahaan data yang didirkan 4 Februari 2004 oleh Mark Zuckerberg yang bergerak dalam layanana jejaring sosial. Facebook erat kaitannya dengan penggunaan Big Data Analytical, yang merupakan suatu proses untuk meneliti serta mengolah data set yang skalanya cukup besar sehingga mendapatkan suatu pola interaksi, serta korelasi suatu hubungan satu dengan yang lainnya.

Advancement in information and communication technology (ICT) has given rise to explosion of data in every field of operations. Working with the enormous volume of data (or Big Data, as it is popularly known as) for extraction of useful information to support decision making is one of the sources of competitive advantage for organizations today. Enterprises are leveraging the power of analytics in formulating business strategy in every facet of their operations to mitigate business risk. Volatile global market scenario has compelled the organizations to redefine their supply chain management (SCM). In this paper, we have delineated the relevance of Big Data and its importance in managing end to end supply chains for achieving business excellence. A Big Data-centric architecture for SCM has been proposed that exploits the current state of the art technology of data management, analytics and visualization. The security and privacy requirements of a Big Data system have also been highlighted and several mechanisms have been discussed to implement these features in a real world Big Data system deployment in the context of SCM. Some future scope of work has also been pointed out.

For quite some time relational database, such as MYSQL ,Oracle and Microsoft SQL Server, have been used to store data for most  Application .With the Web growing rapidly , spawning  enormous ,user-generated content websites such as Facebook ,Twitter, fast database that can handle huge amounts of data are a must .For this purpose new databases management systems collectively called NOSQL are being develop .This thesis explains the NOSQL database ,types of NOSQL ,advantage and disadvantage comparison with MYSQL.NOSQL database is an emerging alternative to the most widely used relational databases.
The massive amounts of data collected today by software in fields varying from academia to business and many other fields, is increasingly becoming a huge problem due to store-age technologies not advancing fast enough to provide the performance scalability needed. This is even more true for data which are highly organized and require analysis while being stored in databases and being accessed by various applications simultaneously. Databases also have the added advantage of providing failover mechanism in case of disruptions or one node failing.
As database vendors struggle to gain more market share new technologies emerge at-tempting to overcome the disadvantages of previous designs while providing more features. Two popular database types, the Relational Database Management Systems and NoSQL databases are examined. The aim of this project was to examine and compare two databases from these two database models and answer the question of whether one performs and scales better than the other.
From the comparison of the results it was found that CouchDB can perform much better for complicated queries at the cost of data duplication which in turn results to a larger database. Also the database size did not appear to be a deciding factor as performance was not crippled significantly for larger database sizes. Writing benchmarks were also run which showed that MySQL performs best at deletion whereas CouchDB excels at inserting documents. The last comparison showed that using sharding to split up the database in CouchDB did not provide a performance advantage which may be related to the routing done by the CouchDB system.
Keywords
Couchdb , couchdb –Java , NoSQL ,Documentation database ,JSON ,DBMS ,MYSQL, Database.

This research review papers shows how HR departments need to optimize the way their organizations use human resources, and to be as efficient as possible themselves. They need to make better and faster decisions, to match people better to requirements, and at the same time to reduce costs. As an HR director, you're probably aware that you could achieve your aims more effectively through better use of data. For example, when a business need arises, you need to be able to see at a glance whether you have the right person available internally or need to look outside. In the latter case, you then typically face the additional challenge of scanning large volumes of applications or CVs/résumés. Fortunately, with today's technology, it's possible to automate much of the work of matching people to requirements. You can also bring together structured and unstructured data to learn more about the potential of your own staff, and take advantage of information available in social networks to find out about potential recruits. With recent advances, all this can be achieved without major investment in technology and related skills. Applying advanced analytics effectively to HR challenges is the aim of our new offer, People Analytics.

Recommender system is the technique which helps the users to fetch usefull and required data according to their interests and preferences by filtering large amount of information. Over the past few years, many recommendation systems for different domains have been developed. Entertainment is one of the significant domain as it is necessary for each one of us to refresh our mood and energy. Movie recommender systems helps users to automatically choose preferred movies from a huge collection. To implement this system, collaborative filtering technique is most widely used as it is more advantageous than other techniques. But this technique also suffers from many issues such as, cold start, data sparsity, shilling attack etc. Many methods have been developed to solve these issues.

Nowadays, Ischemic Heart Disease (IHD) (Heart Attack) is ubiquitous and one of the major reasons of death worldwide. Early screening of people at risk of having IHD may lead to minimize morbidity and mortality. A simple approach is proposed in this paper to predict risk of developing heart attack using smartphone and data mining. Clinical data from 835 patients was collected, analyzed and also correlated with their risk existing clinical symptoms which may suggest underlying non detected IHD. A user friendly Android application was developed by incorporating clinical data obtained from patients who admitted with chest pain in a cardiac hospital. Upon user input of risk factors, the application categorizes the level of IHD risks of the user as high, low or medium. It was found by analyzing and correlating the data that there was a significant correlation of having an IHD and the application results in high & low, medium & low and medium & high categories; where the p values were 0.0001, 0.0001 and 0.0001 respectively. The experimental results showed that the sensitivity and accuracy of the proposed technique were 89.25 % and 76.05 % respectively, whereas, using C4.5 decision tree, accuracy was found 86% and sensitivity was obtained 91.6%. Existing tools need mandatory input of lipid values which makes them underutilized by general people; though these risk calculators bear significant academic importance. Our research is motivated to reduce that limitation and promote a risk evaluation on time.

Data-intensive systems encompass terabytes to petabytes of data. Such systems require massive storage and intensive computational power in order to execute complex queries and generate timely results. Further, the rate at which this data is being generated induces extensive challenges of data storage, linking, and processing. A data-intensive cloud provides an abstraction of high availability, usability, and efficiency to users. However, underlying this abstraction, there are stringent requirements and challenges to facilitate scalable and resourceful services through effective physical infrastructure, smart networking solutions, intelligent software tools, and useful software approaches. This paper analyzes the extensive requirements which exist in data-intensive clouds, describes various challenges related to the paradigm, and assess numerous solutions in meeting these requirements and challenges. It provides a detailed study of the solutions and analyzes their capabilities in meeting emerging needs of widespread applications.

Resumen: El inicio del siglo XXI se caracteriza no solo por continuar la eterna divergencia ideológica en diversos sectores, sino por la aparición de una serie de tecnologías disruptivas capaces de aumentar notablemente el bienestar social tanto como de dar inicio a una crisis socio-política. Gracias al adelanto tecnológico la posibilidad de crear nuevas formas de gobierno han surgido. En este documento se explora el Estado Post-Democrático, una propuesta teórica para evolucionar el sentido democrático de las repúblicas en torno a la cientificidad, el registro de datos anónimo, la gestión de datos con poca intervención humana, la digitalización de la sociedad, la consecución de una Open Society y el camino hacia la transhumanización del hombre.

Clouds provide a powerful computing platform that enables individuals and organizations to perform variety levels of tasks such as: use of online storage space, adoption of business applications, development of customized computer software, and creation of a-realistic‖ network environment. In previous years, the number of people using cloud services has dramatically increased and lots of data has been stored in cloud computing environments. In the meantime, data breaches to cloud services are also increasing every year due to hackers who are always trying to exploit the security vulnerabilities of the architecture of cloud. In this paper, three cloud service models were compared; cloud security risks and threats were investigated based on the nature of the cloud service models. Real world cloud attacks were included to demonstrate the techniques that hackers used against cloud computing systems. In addition, counter measures to cloud security breaches are presented.

Nowadays, Ischemic Heart Disease (IHD) (Heart Attack) is ubiquitous and one of the major reasons of death worldwide. Early screening of people at risk of having IHD may lead to minimize morbidity and mortality. A simple approach is proposed in this paper to predict risk of developing heart attack using smartphone and data mining. Clinical data from 835 patients was collected, analyzed and also correlated with their risk existing clinical symptoms which may suggest underlying non detected IHD. A user friendly Android application was developed by incorporating clinical data obtained from patients who admitted with chest pain in a cardiac hospital. Upon user input of risk factors, the application categorizes the level of IHD risks of the user as high, low or medium. It was found by analyzing and correlating the data that there was a significant correlation of having an IHD and the application results in high & low, medium & low and medium & high categories; where the p values were 0.00...

Collecting, storing and processing of huge amounts of climatic data is necessary for accurate
prediction of weather. Meteorological departments use different types of sensors such as temperature, humidity
etc. to get the values. Number of sensors and volume and velocity of data in each of the sensors makes the data
processing time consuming and complex. Leveraging MapReduce with Hadoop to process the massive amount
of data. Hadoop is an open framework suitable for large scale data processing. MapReduce programming model
helps to process large data sets in parallel, distributed manner. This project aims to build a data analytical engine
for high velocity, huge volume temperature data from sensors using MapReduce on Hadoop

This research review papers shows how HR departments need to optimize the way their organizations use human resources, and to be as efficient as possible themselves. They need to make better and faster decisions, to match people better to requirements, and at the same time to reduce costs. As an HR director, you’re probably aware that you could achieve your aims more effectively through better use of data. For example, when a business need arises, you need to be able to see at a glance whether you have the right person available internally or need to look outside. In the latter case, you then typically face the additional challenge of scanning large volumes of applications or CVs/résumés. Fortunately, with today’s technology, it’s possible to automate much of the work of matching people to requirements. You can also bring together structured and unstructured data to learn more about the potential of your own staff, and take advantage of information available in social networks to fi...

Infosfera e Sistema: cosa resta dell’umano.


Lo sviluppo e, conseguentemente, la diffusione delle tecnologie è attualmente a un tale stato d’avanzamento che persino la riflessione su di esse ne risulta influenzata; lontana da fideismi ingenui, come dai pregiudizi infondati, il pensiero contemporaneo si trova a fare i conti con l’ineliminabilità –  quando non la necessità – dei mezzi tecnologici in grado di coordinare e agevolare le prassi quotidiane di tutti gli attori coinvolti nella globalizzazione, ma anche di fronteggiare le emergenze sanitarie che sono entrate, nostro malgrado, a far parte della nostra recente esperienza collettiva. In Virus sovrano?, la speculazione di Di Cesare sulla pandemia di SARS-CoV-2, che ha colpito il mondo dall’inizio di quest’anno, si pronuncia sul governo degli esperti in termini che possono essere facilmente applicati al governo della tecnologia, quando privata della riflessione etica; leggiamo infatti:
[…] ovunque l’esperto è interpellato […]. Eppure la sua competenza non è una garanzia. Se è un depositario di un sapere specifico, e come tale va ascoltato, non è detto che abbia più esperienza e saggezza di altri. Se conosce alcuni mezzi, non necessariamente vede con lucidità i fini. Può darsi, anzi, che li veda meno di altri. L’esperto è come il timoniere di Agamennone, che riuscì a riportare a casa il suo padrone – dove però fu ucciso.
Considerando la tecnologia come un sapere specifico tra gli altri, vale quindi la pena soffermarsi sulle parole di Di Cesare e di riflettere non solo, sulla miopia dei fini intrinseca alla tecnica, ma specialmente sulle conseguenze antropologiche che questa eccedenza dei mezzi provoca nell’umanità.
Eppure, a partire dalla connessione aristotelica tra intelligenza umana e uso delle mani, per cui è solo l’uomo ad avere nella mano “lo strumento in grado di utilizzare il più gran numero di altri strumenti”  e, conseguentemente, ad essere in grado di impadronirsi – a differenza degli altri animali – del maggior numero di tecniche, molti filosofi – tra cui Adriano Palma – hanno pertinentemente osservato come l’avversione alla tecnica si tradurrebbe in una miopia equivalente a quella della tecnica, andando a criticare quell’animalità di cui l’uomo non riuscirebbe, nemmeno volendo, a liberarsi.
È chiaro che, pur rilevando e ammettendo la distanza tra i possibili punti d’osservazione, qualsiasi speculazione che voglia apportare un contributo al tema della mutazione antropologica contemporanea, per effetto della tecnica, debba nutrirsi di una letteratura sempre più specifica,  per comprendere la portata delle attuali scoperte e per collocarsi in un territorio di neutralità vigile e cosciente, ricco di interrogativi e al riparo da quelle risposte pronte a dimostrarsi insufficienti, se indagate dal punto di vista dell’operatività collettiva.
A tal proposito, il nostro contributo si presenta come una rilettura della relazione tra sistema e ambiente, analizzata da Luhmann in Sistemi sociali, alla luce del concetto di infosfera, elaborato da Floridi. Se infatti, l’ambiente non è una categoria residuale, ma il presupposto dell’identità del sistema  – anche quando si costituisce in termini differenziali – e Floridi definisce l’infosfera attuale come “l’intero ambiente informazionale costituito da tutti gli enti informazionali, le loro proprietà, interazioni, processi e reciproche relazioni”,  ma anche come “realtà” – a patto che si intenda quest’ultima in termini puramente informazionali – è chiaro fino a che punto un confronto tra i termini ci aiuterà a delineare i tratti dell’individualità contemporanea. Non meno importante: la capacità di auto-osservazione da parte di ogni sistema, ci dimostra la capacità del sistema di analizzare il rapporto che esso intrattiene con l’ambiente; poiché tale rapporto si delinea in termini di differenziazione e ha come finalità sistemica la riproduzione etopoietica, un’indagine sui caratteri dell’infosfera e sulla necessità antropologica di accompagnare eticamente lo sviluppo tecnologico sarebbe coerente alla proceduralità del sistema e, in ultima analisi, funzionale allo sopravvivenza dello stesso.

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.

Big data is dataset that having the ability to capture, manage & process the data in elapsed time .Managing the data is the big issue. And now days the huge amount of data is produced in the origination so the big data concept is in picture. It is data set that can manage and process the data. For managing the data the big data there are many technique are used .One of this technique is Hadoop. Hadoop can handle the huge amount of data, it is very cost effective, and it can handle huge amount of data so processing speed is very fast, and also it can create a duplicate copy of data in case of system failure or to prevent the loss of data.This paper contains the Introduction of big data and Hadoop, characteristics of big data ,problem associated with big data, architecture of big data and Hadoop, other component of hadoop, advantages, disadvantages and applications of Hadoop and also the conclusion.

Advancement in information and communication technology (ICT) has given rise to explosion of data in every field of operations. Working with the enormous volume of data (or Big Data, as it is popularly known as) for extraction of useful information to support decision making is one of the sources of competitive advantage for organizations today. Enterprises are leveraging the power of analytics in formulating business strategy in every facet of their operations to mitigate business risk. Volatile global market scenario has compelled the organizations to redefine their supply chain management (SCM). In this paper, we have delineated the relevance of Big Data and its importance in managing end to end supply chains for achieving business excellence. A Big Data-centric architecture for SCM has been proposed that exploits the current state of the art technology of data management, analytics and visualization. The security and privacy requirements of a Big Data system have also been highlighted and several mechanisms have been discussed to implement these features in a real world Big Data system deployment in the context of SCM. Some future scope of work has also been pointed out.

3rd International Conference on Big Data & IoT (BDIoT 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Big Data and IoT. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Big Data, Internet of Things and advances.3rd International Conference on Big Data & IoT (BDIoT 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Big Data and IoT. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Big Data, Internet of Things and advances.

Recommendation systems have become an important role in today’s digital world. During the last few decades, with the
rise of YouTube, Amazon, Netflix and many other such web services, recommender systems have taken more and more place in
our lives. From e-commerce (suggest to buyers’ articles that could interest them) to online advertisement (suggest to users the
right contents, matching their preferences), recommender systems are today unavoidable in our daily online journeys. Users get
more data related to them using these systems. Here, we are developing a recommendation system to suggest movies to users.

The term Big Data is being used widely by companies and researchers who consider your relevant functionalities or applications to create value and business innovation. However, some questions arise about what is this phenomenon and, more precisely, how it occurs and under what conditions it can create value and innovation in business. In our view, the lack of depth related to the principles involved in Big Data and the very absence of a conceptual definition, made it difficult to answer these questions that have been the basis for our research. To answer these questions we did a bibliometric study and extensive literature review. The bibliometric studies were realized based in articles and citation of Web of Knowledge database. The main result of our research is the providing a conceptual definition for the term Big Data. Also, we propose which principles discovered can contribute with other researches that intend value creation by Big Data. Finally we propose see the value creation through Big Data using the Resource Based View as the main theory used for discuss that theme.

3rd International Conference on Machine Learning Techniques and Data Science (MLDS 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning Techniques and Data Science

Internet of Things (IoT) is changing the physical world. Billions of smart objects across the world generate sensed data about the environmental changes and report them. These sensed data create a lot of traffic in the network, a lot of redundancy, and duplicated records, and increase the storage size in BigData in cloud computing. Some of the sensed data are required to perform a real-time action. In this paper, we propose a framework to provide the decision makers in today's organizations with real-time data that could help them make better decisions. In addition, it could filter and fuse the data with Adaptive Sampling rate using Feedback control mechanism (FCM) in the network layer. The proposed framework will reduce the number of IoT records. Moreover, it avoids a lot of traffic in the network, increases the network life, and minimizes the energy consumption. The proposed approach can help most of Businessmen in their decisions, increases the performance and reduces the cost. It can also be applied to any Businesslike ERP, CRM, and SCM. We have implemented and tested it by SQL and CQL. The results of the experiments show that the reduction has been achieved by 82.34 percent compared to the base one. This reduction has improved the energy consumption in the traffic in the network layer, generating an efficient BigData in terms of non-noise, storage capacity and cost.

This work is based on information shared by many researchers. The understanding of misinformation as a phenomenon Datos masivos, algoritmización y nuevos medios frente a desinformación y fake news. Bots para minimizar el impacto en las organizaciones Big data, algorithmization and new media against misinformation and fake news. Bots to minimize the impact on organizations Resumen

Éste trabajo parte de una información compartida por muchos investigadores. La comprensión de la desinformación como un fenómeno que va mucho más allá del término "noticias falsas". Estos términos han sido apropiados y usados engañosamente por poderosos actores para desestimar y poner en entredicho la cobertura informativa que ya atraviesa momentos críticos sobre la credibilidad. La desinformación, como abordamos en este artículo, incluye todas las formas de lo falso, información inexacta o engañosa, diseñada, presentada y promovida para causar intencionalmente daño público o con fines de lucro. Para contrarrestar este fenómeno, organizaciones y gobiernos vienen promoviendo diversas iniciativas. Muchas de estas iniciativas recalan en la inteligencia artificial que con el arte de los algoritmos desarrolla netbots y plataformas con el objetivo de luchar contra la toxicidad de la información. Por su parte, las organizaciones de noticias vienen implantando unidades de trabajo especializado cuyos profesionales ostentan un perfil multi-interdisciplinar con capacidad para utilizar diversas técnicas de big data y herramientas de filtrado y visualización de datos. El artículo analiza los principales desarrollos de bots utilizados para minimizar el impacto de las noticias falsas.

Big Data analytics help extract information and knowledge from data sets that are useful for businesses in making the right, efficient decisions so they can improve company performance. But knowledge management gained from the big data analytics process is rarely investigated further, despite the insistence on the need for a structured and integrated approach. This paper aims to discuss modeling using big data analytics to measure its impact on the performance of national startup companies in Indonesia. The author also examines the role of knowledge management as a mediator of the relationship between big data analytics and company performance. Data were collected from 10 national startups and used structural equation modeling approaches. The original of this research is the potential to change the way startups grow, develop and survive through understanding, processing and utilizing large amounts of data sourced from their own data. Some managerial and theoretical implications are proposed and discussed in light of the emergence of this startup companies phenomenon.

Flight delay has been the fiendish problem to the world's aviation industry, so there is very important significance to research for computer system predicting flight delay propagation. Extraction of hidden information from large datasets of raw data could be one of the ways for building predictive model. This paper describes the application of classification techniques for analysing the Flight delay pattern in Egypt Airline's Flight dataset. In this work, four decision tree classifiers were evaluated and results show that the REPTree have the best accuracy 80.3% with respect to Forest, Stump and J48. However, four rules based classifiers were compared and results show that PART provides best accuracy among studied rule-based classifiers with accuracy of 83.1%. By analysing running time for all classifiers, the current work concluded that REPtree is the most efficient classifier with respect to accuracy and running time. Also, the current work is extended to apply of Apriori association technique to extract some important information about flight delay. Association rules are presented and association technique is evaluated.

Clouds provide a powerful computing platform that enables individuals and organizations to perform variety levels of tasks such as: use of online storage space, adoption of business applications, development of customized computer software, and creation of a-realistic‖ network environment. In previous years, the number of people using cloud services has dramatically increased and lots of data has been stored in cloud computing environments. In the meantime, data breaches to cloud services are also increasing every year due to hackers who are always trying to exploit the security vulnerabilities of the architecture of cloud. In this paper, three cloud service models were compared; cloud security risks and threats were investigated based on the nature of the cloud service models. Real world cloud attacks were included to demonstrate the techniques that hackers used against cloud computing systems. In addition, counter measures to cloud security breaches are presented.

The use of Big Data analytics for business improvements is a vital strategy for survival. In this paper, we report a study that investigates the role of BD analytics on consumer finance, credit card finance in China—a research area that has largely remained unexplored. The largeness and diversity of Chinese consumer market merit an urgent attention and understanding of role of BD analytics is significant both theoretically and managerially. This study achieves that target. Given the exploratory nature of study, we take a qualitative approach. We conduct approximately 30 interviews with baking and finance sector respondents. The data will be recorded, transcribed and translated. We will analyze data using content analysis / thematic analysis technique.

Big data is a collection of large amount of data with various types of data and usable to be processed at much higher frequency. One of the most popular knowledge discovery approaches is to find frequent items from a transaction data set and derive association rules. Pattern finding is one of the most computationally expensive steps in large data sets. Patterns often referred to association rules. Association rule plays an important role in the process of mining data for sequential pattern. Association rules are used to acquire interesting rules from large collections of data which expresses an association between items or sets of items. Apriori is a classic algorithm for learning association rules. It is designed to operate on databases containing transactions. Apriori algorithm attempts to find subsets which are common to at least a minimum number C of the item sets. Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time and groups of candidates are tested against the data. The algorithm terminates when no further Successful extensions are found. In this paper we enhance Apriori algorithm to solve its complexity over large data sets. We first collect variety of data and then integrate both structured and unstructured data using MapReduce to find out sequential pattern from the required data sets.