big data

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Big data - meta data

Big data - meta data

Everything in the world seems to be on a fast pace nowadays or moving towards irregular directions. Weather, for example, is changing, hence the need for advanced computing together with big data in order to fully forecast. On the other hand, humans are not easily predictable, so organizations are taking advantage of big data to stay in business or keep pace with competition

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BIG DATA AND MANAGEMENT

BIG DATA AND MANAGEMENT

Big data can also be a potent tool for analysis of individual or team behavior, using sensors or badges to track individuals as they work together, move around their workspace, or spend time interacting with others or allocated to specific tasks. While early management research codified diaries and time-man- agement techniques of CEOs, evolving practices— using big data— can allow us to study entire organi- zations and workgroups in near-real time to predict individual and group behaviors, team social dynam- ics, coordination challenges, and performance out- comes. Scholars could examine questions around the differences between stated versus revealed prefer- ences by tracking data on purchasing, mobile appli- cations, and social media engagement and consump- tion, to state but a few examples. Social network studies could also use big data to examine the dynam- ics of formal and informal networks as they form and evolve, as well as their impact on individual, net- work, and organizational behaviors. Such granular, high-volume data can tell us more about workplace practices and behaviors than our current data-collec- tion methods allow—and have the potential to trans- form management theory and practice.

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Big Data Computational Intelligence Networking 4 pdf  pdf

Big Data Computational Intelligence Networking 4 pdf pdf

Every day, people surf the Internet, browse websites, post messages, and share personal information like cookies through their own digital devices. This can pose a potential threat to privacy issues through identity theft, online banking abuse, password leakage, and so on. Information security suffers from the prolif- eration of devices connected to the Internet and connected with each other. Big data security issues are related to a set of risk areas, which includes information lifecycle, data creation, and collection processes. Despite complex initial char- acteristics, ultimately, the objective remains the same as traditional data types, devoted to preserving confidentiality, integrity, and availability. Due to user/data mobility requirements, conventional security measures such as firewalls are no longer suitable for big data scenarios. Cloud Security Alliance (CSA) [30], a non-profit organization with a mission to promote the use of best practices for providing security assurance within Cloud Computing and provide education on the uses of Cloud Computing to help secure all other forms of computing, has created a Big Data Working Group, which has focused on the major challenges to implement secure Big Data services. According to CSA, security and privacy challenges can be categorized according to four aspects: infrastructure security (e.g., secure distributed computation using MapReduce), data privacy (e.g., data mining that preserves privacy/granular access), data management (e.g., secure data provenance and storage), and integrity and reactive security (e.g., real-time monitoring of anomalies and attacks).

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Big Data : Securing the Data

Big Data : Securing the Data

Hadoop, like many open source technologies was not created with security in mind. Its ascension amongst corporate users has invited more focus, and as security professionals have continued to point out potential security vulnerabilities and Big Data security risks with Hadoop, this has led to continued security modi fi cations of Hadoop. There has been an explosive growth in the ‘Hadoop security’ market, where vendors are releasing ‘security-enhanced’ distributions of Hadoop and solutions that promise an increased Hadoop security. However, there are a number of security challenges for organisations securing Hadoop that are shown in Figure 1.

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Big Data vs Open Data

Big Data vs Open Data

McGraw-Hill el 10 de enero - he tenido que pensar y explicar cómo Open Data, Big Data y Open Government están relacionados entre sí. Últimamente he visto un número de otros, como los autores del nuevo informe de McKinsey Open Data (ver página 4), tratar de mapear el territorio de manera similar. La comunidad Open Data está produciendo una gran cantidad de diagramas de Venn en estos días, con una gran cantidad de círculos que se superponen colores. (Algunos también se ocupan de la utilización de los datos personales, pero eso es un círculo demasiado para mí.)

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Big Data in Business

Big Data in Business

Data can create value for businesses, as insight gained from its analysis can inform decisions. Traditionally, companies have used business intelligence tools for data analysis. These are applied to structured data, which reside in fixed fields, as in a spreadsheet. By comparison, ‘big data’ analytics typically involve data from a wider variety of sources, which may be rapidly analysed as they are collected (in ‘real-time’). These data are often unstructured, such as images or videos. There is no single official definition of big data, but big data specialists often refer to its high volume, velocity and variety. These features make it difficult to readily manage and analyse using desktop computers and traditional data management tools. Use of big data analytics creates opportunities to increase efficiency and improve predictions, leading to new, more personalised, products and services. It has also led to the creation of new data-driven business models, such as those used by Facebook and Tesco. Estimates suggest that, via efficiencies, innovation and business creation, big data was worth £25 billion to UK businesses in 2011, and may reach an annual value of £41 billion by 2017. 1 The Government

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Big Data Approach Paper

Big Data Approach Paper

The big data is not to be considered as a single set of data, but it is the way data grow and when there is some kind realization in the ways that might be able to connect different sets of data together to create even more sets of information. In a nut shell, Big data represents the ever – expanding collection of data sets that sheer size, variety and speed of generation make difficult to manage and harness information from.

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Data Modeling for Big Data

Data Modeling for Big Data

making [2]. This definition challenges is twofold. The first is about cost-effective innovative forms of information processing. And the second is enhanced insight and decision making [3]. Big data is fundamentally about applying innovative and cost-effective techniques for solving existing and future business problems whose resource requirements (for data management space, computation resources, in memory representation needs) exceed the capabilities of traditional computing environments as currently configured within the enterprise. The problem happened in early 2000s when data volumes started skyrocketing, storage and CPU technologies were overwhelmed by the numerous terabytes of big data to the point that Information Technology faced a data scalability crisis. The Enterprise, then, went from being unable to afford or manage big data to lavishing budgets on its collection and analysis.

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Networking for Big Data Chapman pdf  pdf

Networking for Big Data Chapman pdf pdf

The cloud computing paradigm enables rapid on-demand provisioning of server resources (CPU, storage, bandwidth) to users, with minimal management efforts, as witnessed in Amazon EC2 and S3, Microsoft Azure, Google App Engine, Rackspace. The elastic and on- demand nature of resource provisioning makes a cloud platform attractive for the execu- tion of various applications, especially computation-intensive ones [1,2]. More and more data-intensive Internet applications, such as Facebook and Twitter, and Big Data analytics applications, such as the Human Genome Project [3], are relying on clouds for processing and analyzing their petabyte-scale data sets, leveraging a computing framework such as MapReduce and Hadoop [4,5]. Facebook-like social media sites collect their web server logs, Internet click data and social activity reports from geographical locations over time, and parse them using MapReduce/Hadoop to discover usage patterns and hidden correla- tions, in order to facilitate decision making in marketing. In such data-intensive applica- tions, a large volume of information (up to terabytes or even petabytes) is periodically transmitted between the user location and the cloud, through the public Internet. Parallel to utility bill reduction in data centers (computation cost control), bandwidth charge min- imization (communication cost control) now represents a major challenge in the cloud computing paradigm [6,7], where a small fraction of improvement in efficiency translates into millions of dollars in annual savings across the world [8].

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Big Data and Peacebuilding

Big Data and Peacebuilding

Though big data is a big story today, the extent to which it is inextricably entwined in peacebuilding and peacekeeping is yet to be robustly explored. Without such sound study, it cannot be easily assumed that big data – its existence and consumption – always serves to strengthen peace. It may well be the case the big data can contribute more to destabilisation in fragile democra- cies, especially those with low media literacy and entrenched propaganda channels. Those supporting violence will possibly champion big data as much as those interested in how this information can be packaged for peace. How to fully leverage this progressive, non- violent potential of big data to save lives and build peace will be one of the defining, most important challenges in this century. 3

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Networking Big Data Chapman Hall 6846 pdf  pdf

Networking Big Data Chapman Hall 6846 pdf pdf

The cloud computing paradigm enables rapid on-demand provisioning of server resources (CPU, storage, bandwidth) to users, with minimal management efforts, as witnessed in Amazon EC2 and S3, Microsoft Azure, Google App Engine, Rackspace. The elastic and on- demand nature of resource provisioning makes a cloud platform attractive for the execu- tion of various applications, especially computation-intensive ones [1,2]. More and more data-intensive Internet applications, such as Facebook and Twitter, and Big Data analytics applications, such as the Human Genome Project [3], are relying on clouds for processing and analyzing their petabyte-scale data sets, leveraging a computing framework such as MapReduce and Hadoop [4,5]. Facebook-like social media sites collect their web server logs, Internet click data and social activity reports from geographical locations over time, and parse them using MapReduce/Hadoop to discover usage patterns and hidden correla- tions, in order to facilitate decision making in marketing. In such data-intensive applica- tions, a large volume of information (up to terabytes or even petabytes) is periodically transmitted between the user location and the cloud, through the public Internet. Parallel to utility bill reduction in data centers (computation cost control), bandwidth charge min- imization (communication cost control) now represents a major challenge in the cloud computing paradigm [6,7], where a small fraction of improvement in efficiency translates into millions of dollars in annual savings across the world [8].

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Buku Dan Analisis Big Data

Buku Dan Analisis Big Data

Alhamdulillahhi robbil alamin, puji syukur kehadirat Allah SWT atas segala rahmat dan karunia-Nya dengan terselesaikannya penu- lisan buku ini dengan judul “ Analisis Big Data ”. Buku ini merupakan uraian untuk memudahkan pemahaman konsep, tingkat dasar sampai lanjut dalam sistem cerdas dan penerapannya melalui pemanfaatan teknologi Big Data, dengan mengedepankan keterampilan dalam pembuatan dan hasil implementasi dengan berbagai kombinasi algoritma berbasis sistem cerdas maupun dengan perpaduan berbagai macam tools untuk membangun ekosistem analisis Big Data yang powerfull. Konsep sederhana Analisis Big Data mencakup Volume, Ve- locity, dan Variety (3V), dan pengembangannya ada yang menyebut (7V) termasuk Volume, Velocity, Variety, Variability, Veracity, Value dan Visualization, atau 10V bahkan lebih dari itu, tetapi sebaiknya tidak membatasi pemahaman Big Data ini hanya dengan sedikit banyaknya istilah dari huruf V yang digunakan . Kemudian dengan adanya ekosistem tersebut, jika dibutuhkan analisis sederhana maupun yang lebih kompleks, maka harapannya tidak akan ada kendala dari besarnya data yang diolah. Adanya kemajuan teknologi dalam hal penyimpanan, pengolahan, dan analisis Big Data meliputi (a) penurunan secara cepat terhadap biaya penyimpanan data dalam be- berapa tahun terakhir; (b) fleksibilitas dan efektivitas biaya pada pusat data dan komputasi awan untuk perhitungan dengan konsep elastisitas dan penyimpanannya; serta (c) pengembangan kerangka kerja baru seperti Hadoop ecosystem (salah satu peluang bisnis yang besar untuk developer untuk saat ini dan ke depannya dalam rangka membangun ekosistem analisis Big Data yang sangat powerfull sekelas Cloudera, Hortonworks, etc), yang memungkinkan pengguna untuk mengambil manfaat dari sistem komputasi terdistribusi, misal untuk menyimpan sejumlah data yang besar melalui pemrosesan parallel, dukungan da- tabase NoSQL, dan komputasi berbasis streaming. Sehingga kema- juan teknologi ini telah menciptakan beberapa perbedaan yang sangat signifikan, misal dalam hal kecepatan maupun ketepatan dari hasil yang didapatkan antara analisis tradisional dengan tools yang bukan dengan konsep Big Data versus analisis modern untuk saat ini dan masa depan dengan membangun ekosistem Big Data yang sangat powerfull.

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Big data ja Porthan

Big data ja Porthan

AURAICA 7, 2016 Big data ja Porthan 95 Runon mukaan koskenperkaaja oli avannut esteet kansallisuuden ”koskelta”, joka saattoi nyt kuohua ”maltitonna”. Yhtä kuohuvaa ja esteetöntä oli lopulta suomalainen sanomalehdistö, josta 1800-luvun kuluessa kasvoi moniaineksinen, moneen suuntaan haarautuva rihmasto, jonka silmukoiden ja risteyskohtien, umpikujien ja jatkumoiden tutkimus tempaa mukaansa.

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Big Data Paper

Big Data Paper

The variety component of Big Data has become the most challenging issue regarding analyzing Big Data because unstructured data is difficult to break down and organize due to all the different sources of data it contains. Approximately 90% of a company’s data consists of unstructured data. As mentioned before, unstructured data includes files containing text and multimedia components including emails, photographs, music files, websites, etc. Although unstructured data procures the majority of all existent data, it has presented the most difficulty for the IT community to design software to analyze it. The challenge lies within filtering relevant data from multiple mediums and then organizing it into

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Optimisasi Dengan Adanya Big Data Problem

Optimisasi Dengan Adanya Big Data Problem

Michael Cox dan David Ellsworth tahun 1997. Persoalan yang muncul menge- nai big data dinyatakan dalam rumusan berikut, hal yang utama adalah terda- pat pertumbuhan data dan informasi yang sangat eksponensial, kecepatan dalam pertambahannya dan semakin bervariasinya data tersebut yang dikemudian hari menciptakan tantangan baru bagi yang tidak hanya tantangan dalam pengelo- laan sejumlah besar data yang heterogen, tetapi juga bagaimana untuk mema- hami semua data tersebut. Didalam lingkungan organisasi juga mulai tumbuh sejumlah pegawai/staf yang secara spesifik mendapat sebutan sebagai analis bis- nis/data analis/ilmuwan data yang dalam aktifitas bekerjanya memanfaatkan per- alatan yang modern, melakukan praktek dan mencari solusi dari suatu data. 2.1.2 Defenisi big data

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Big Data  Understanding How Data Powers Big Business pdf  pdf

Big Data Understanding How Data Powers Big Business pdf pdf

Real-time data access and analysis requirements: Certain use cases are going to require real- time (or low-latency) data access, analysis, and decision making as data is flowing through the business. These real-time requirements must be addressed across your entire technology and architectural stack including your Extract, Transform, and Load (ETL) and Extract, Load, and Transform (ELT) algorithms, data transformation and enrichment processes, in-memory computing, complex event processing, data platform, analytic models, and user experience. Data management capabilities: The big data industry has gained lots of experience and has developed many excellent tools and methodologies for helping organizations in the data management space (such as, master data management, data quality, and data governance). However, organizations also need to address when the data quality is good enough given the types of decisions and business processes that are being supported. Organizations need to carefully think through this question so that time is not wasted trying to make imperfect data perfect, especially when the decisions and the business processes that the data will support do not need perfection (for example, ad serving, fraud detection, location-based marketing, and markdown management). This part of the solution requires understanding and answering the “When is 90-percent accurate data good enough?” question.

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Optimisasi Dengan Adanya Big Data Problem

Optimisasi Dengan Adanya Big Data Problem

Puji Syukur kehadirat Allah SWT yang selalu memberikan rahmat dan hi- dayahNYA sehingga penulis dapat menyelesaikan tesis yang berjudul ”OPTI- MISASI DENGAN ADANYA BIG DATA PROBLEM”. Tesis ini meru- pakan salah satu syarat untuk menyelesaikan studi pada Program Studi Magister Matematika Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) Univer- sitas Sumatera Utara.

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Big Data Security Management

Big Data Security Management

Although there are many challenges in big data management and applications, there are also many op- portunities presented by big data that lead to new discoveries, insights, and innovative applications. In the security industry, big data analytics has helped organizations to proactively identify and predict security attacks (Hipgrave, 2013). With the advancement of big data analytics, organizations will be able to collect and analyze large volume of data from various unstructured data sources such as email, social media, e-commerce transactions, bulletin boards, surveillance video feeds, Internet traffic, etc. Forward-looking organizations are moving beyond traditional security measures to safeguard their coveted information assets. Big data security monitoring and analytical tools can be a valuable aid to security professionals and law enforcement in their fighting against cyber criminals. An example of such an application is to analyze data in confluence and in motion to detect cyber fraud/attack before serious damage occurs.

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Sentiment Analysis Berbasis Big Data Sentiment Analysis Based Big Data

Sentiment Analysis Berbasis Big Data Sentiment Analysis Based Big Data

analysisdata keluhan masyarakat yang masuk. Manfaat sentiment analysis sangat penting untuk mengetahui sejauh mana data keluhan masyarakat terhadap pembangunan serta digunakan sebagai alat bantu untuk melihat respon masyarakat terhadap pembangunan kota Surabaya. Mengingat jumlah data keluhan yang masuk begitu besar maka diperlukan sebuah proses analisa data yang mampu menangani hal ini. Salah satu alternatif yang tersedia saat ini adalah menggunakan analisa big data.Karakteristik data sumber dari analisa big data adalah data yang memiliki 3 karakteristik yaitu volume(ukuran data yang besar), variety(tipe datanya bervariasi dari data tidak terstruktur dan data terstruktur) dan velocity(transaksi data dalam jumlah yang besar). Ini sesuai sekali dengan profil dari data website media center Pemkot Surabaya.

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BIG DATA MANAGEMENT

BIG DATA MANAGEMENT

In so doing, the defense misses out on critical benefits that virtualization brings. One of the main benefits of virtualization in managing big data is the creation of self-service in access of information saving both time and money. These technologies enable the integration of data from various sources, sorting and analysis of the big data and conversion into relevant information depending on the request made. This ensures increased efficiency and effectiveness in

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