Statistics takes important role in big data because many statistical methods are used for big data analysis. Statistical software provides rich functionality for data analysis and modeling, but it can handle only limited small amounts of data. Regression can be seen in many areas widely used such as business, the social and behavioral sciences, the biological sciences, climate prediction, and so on. Regression analysis is applied in statistical big data analysis because regression model itself is popular in data analysis. There are two approaches for big data analysis using statistical methods like regression. The ﬁrst approach is that we consider extracting the sample from big data and then analyzing this sample using statistical methods. This is actually the traditional statistical data analysis approach assuming that big data as a population. Jun et al.  already expressed that in statistics, a collection of all elements which are included in a data set can be deﬁned as a population in the respective ﬁeld of study. That is why; the entire population cannot be analyzed indeed according to many factors such as computational load, analyzing time and so on. Due to the development of computing environment for big data and decreasing the cost of data storage facilities, big data which close to the population can be analyzed for some analytical purposes. However, the computational burden still exists as a limitation in analyzing big data using statistical methods. The second approach is that we consider about splitting the whole big data set into several blocks without using big population data. The classical regression approach is applied on each block and then respective regression outcomes from all blocks are combined as ﬁnal output . This is only a sequential process of reading and storing data in primary memory block by block. Analyzing data in each block separately may be convenient whenever the size of data is small enough for implementing the estimation procedure in various computing environments. However, a question, how to replace sequential processing of several data blocks that can adversely affect in response time still remains as an issue for processing increasing volume of data . Jinlin Zhu, Zhiqiang Ge and et al. proved that MapReduce framework is a sort of resolution to this problem for the replacement of sequential processing with the use of parallel distributed computing that enables distributed algorithms in parallel processing on clusters of machines with varied features.
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In the 2016 Higher Competitive Grants Research (Hibah Bersaing Dikti), we have been successfully developed models, infrastructure and modules of Hadoop-based big data analysis application. It has also successfully developed a virtual private network (VPN) network that allows integration and access to the infrastructure from outside the FTIS Computer Lab. Infrastructure and application modules of analysis are then wanted to be presented as services to small and medium enterprises (SMEs) in Indonesia. This research aims to develop application of big data analysis service interface integrated with Hadoop-Cluster. The research begins with finding appropriate methods and techniques for scheduling jobs, calling for ready-made Java Map-Reduce (MR) application modules, and techniques for tunneling input / output and meta-data construction of service request (input) and service output. The above methods and techniques are then developed into a web- based service application, as well as an executable module that runs on Java and J2EE based programming environment and can access Hadoop-Cluster in the FTIS Computer Lab. The resulting application can be accessed by the public through the site http://bigdata.unpar.ac.id. Based on the test results, the application has functioned well in accordance with the specifications and can be used to perform big data analysis.
two methods: Variant Recalibrate and Variant Filtration. Variant Recalibrate uses the machine learning method to train known public variants for recalibrating variants. Variant Filtration uses the fixed thresholds for filtering variants. If you have diploid and enough depth of coverage variants like our example below, Haplotype Caller and Variant Recalibrate are recommended in your analysis. In addition to these, other softwares can also serve the same purpose. FreeBayes uses Bayesian genetic variant detector to find SNPs, Indels, and com- plex events (composite insertion and substitution events) smaller than reads length. In the tutorial of this chapter, we also use Galaxy freeBayes as an example. When the alignment BAM file is loaded, it will report a standard variant VCF file. Another important vari- ant discovery is to detect genomic copy number variation and struc- ture variation. VarScan is a software to discover somatic mutation and copy number alteration in cancer by exome sequencing. At first, samtools mpileup uses disease and normal BAM files to generate a pileup file. And then, VarScan copy number will detect copy number variations between disease and normal samples. VarScan copy Caller will adjust for GC content and make preliminary calls. ExomeCNV is a R package software, which uses depth of coverage and B-allele for detecting copy number variation and loss of heterozygosity. GATK depth of coverage will be used to convert BAM file into cov- erage file. Afterward, ExomeCNV will use paired coverage files (e.g., tumor-normal pair) for copy number variation detections. Copy number variations will be called on each exon and large segments one chromosome at a time. BreakDancer has been used to predict wide-variety of SVs including deletion, insertion, inversion, intra- chromosomal translocation, and interchromosomal translocation. BreakDancer takes alignment BAM files as input, and bam2cfg will generate a configure file. Based on configure file, BreakDancerMax will detect the five structure variations in the sample.
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Technology to use and analyze the information widely available, but many companies are taking a new level data using IT to support appropriately, directing decisions and test new products, business models, and innovation to the customer experience, in some cases this approach helps companies to make decisions in real time. The company sells physical products also use big data to the appropriate experiments. Use the information to analyze new business opportunities, such as the effective promotion of the right segment. Other companies collect data from social networks in real time (Ford Motor, PepsiCo and Southwest Airlines). The use of experimental and big data as an important component in management decision making requires new capabilities, as well as organizational and cultural change, most companies away from accessing all the data available. Generally, the company does not have the right talent and processing for designing experiments and extract business value from big data, which requires a change in the way many executives who currently make a decision: to trust instinct / instinct and experience during the experiment and rigorous analysis. Big data over time will be a new type of company assets, which indicates an important key to the competition. If it is true, the company should start thinking seriously whether they are organized to exploit the potential of large data (big data) and to manage the threats that may arise. Success will require not only new skills, but also a new perspective on how to revolutionize the era of big data-expanding circle of management practices that can influence and foundations represent novelty, potential business models (disruptive) .
programming explicit algorithms with good performance is difficult or unfea- sible. There is a large amount of research taking place to combine advanced machine learning techniques with networked big data analysis. Shang et al.  suggested a novel fault diagnosis model of computer networks based on rough set and back-propagation (BP) neural networks. They retrieved faults as a series of rules and reduced them to a minimum diagnosis rules by rough set theory; then a neural network was designed to learn from these rules in order to identify and localize faults quickly and accurately. Sankaran et al.  designed a hard- ware framework embedded with a machine learning coprocessor to emphasize intrusion detection in case of attacks unknown for the signature library. They chose a vector space model with a K-nearest neighbors (K-NN) classifier and a radial basis function (RBF)-based classifier, concluding that machine learning processors enable improvement in energy saving, processing speed, and detec- tion accuracy, especially dealing with big data sets. Chung et al.  addressed the significant advantages that deep neural networks (DNNs) demonstrate in pat- tern recognition applications, regardless of the biggest drawback of computation resource consumption and training time reaching a tenfold addition compared to traditional techniques. In order to cope with this, a data-parallel Hessian-free second-order optimization algorithm is used to train DNN and implemented by a large-scale speech task on Blue Gene/Q computer system due to the excellent interprocessor communication ability of this system. The results show that per- formance on Blue Gene/Q scales up to 4096 processes lossless in accuracy and that enables the training of DNN using billions of inputs in just a few hours.
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Deming (, p. 106) said that “Knowledge comes from theory. Without theory, there is no way to use the information that comes to us on the instant”. The Deming quote relating to knowledge may not sit that well with many data mining approaches that search for something interesting in the data. Theory we think is formulated by past observations generating beliefs that are tested by well planned studies, and only then integrated into knowledge when the belief has been “proven” to be true. Data is certainly not information—it has to be turning into information. Many data mining methods are rather short on theory but they still aim to turn data into information. We believe that data mining plays an important role in generating beliefs that needed to be integrated into a theoretical frame which we will call knowledge. When modelling data statisticians sometimes find these theoretical frameworks are too restrictive. At times statisticians make assumptions that have theoretical foundations which are practically unrealistic. This is generally used to make progress towards solving a problem and it is a step in the right direction, but not the appropriate solution. Eventually over time someone builds on this idea and the problem can then be solved without unrealistic assumptions. This is how the theoretical framework is extended to solving the more difficult problems. Non-statistically trained data-miners we believe too often drop the theoretical considerations. Some data-miners attempt to transform data into information using common sense and make judgments about knowledge called learning from the data—sometimes they may get it wrong but often they may be right. Have we statisticians got too hung-up about theory? We do not think so. We may assume too much at first in trying to solve a problem but our foundations are the theory. The current Big Data initiatives are mostly based on the assumption that Big Data is going to drive knowledge (without a theoretical framework). We disagree with this assertion and believe the solution is for data-miners and statistician to collaborate in the process of generating knowledge within a sound theoretical framework. We believe that statisticians should stop making assumptions that remain unchecked and data-miners should work with statisticians in helping discover knowledge that will help manage the future. It is knowledge that helps us improve the management of the future and this should be our focus.
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Buku ini memberi pemahaman konsep dasar, maupun tingkat lanjut secara mendetail sebagai solusi dalam penyelesaian untuk kasus apapun dengan konsep Sistem Cerdas melalui pemanfaatan teknologi Big Data mulai dari tingkat dasar sebagai cara yang paling mudah untuk awalan dalam pemahaman, sampai pada implementasi algoritma tanpa Library apapun, misal tidak menggunakan MLlib dari Spark sama sekali atau lainnya, serta melatih dalam memodifikasi algoritma maupun penggabungan dua tools atau lebih untuk membangun ekosistem Big Data yang powerfull. Materi yang tersedia selain memudahkan bagi para pembaca, juga untuk mendukung ma- teri perkuliahan yang dapat membantu pengayaan mahasiswa yang fokus pada pengembangan Artificial Intelligence (AI) untuk Big Data, yang meliputi banyak Machine Learning yang digunakan.
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Data fusion and data integration is together a set of techniques for business intelligence that is used to integrate online storage for catalog of sales databases to create more com- pleted pictures for the customers. Taking the example of this Williams Sonoma , an American consumer retail company has integrated customer databases with information on 60 million households. Variables including household income, housing values, and num- ber of children needs are tracked. It is claimed that targeted emails based on this informa- tion yield 10–18 times the response rate of emails that are not targeted. This is a simple illustration of how more information can lead to better inferences. Such techniques that can help preserve privacy are emerging . There is a great amount of interest today in multi- sensor data fusion. The biggest technical challenges being tackled today, generally through development of new and better algorithms, related to data precision and resolution: Outliers and spurious data, conflicting data, modality both heterogeneous, homogeneous data and dimensionality, data correlation, data alignment, association within data, centralized ver- sus decentralized processing, operational timing, and the ability to handle dynamic versus static phenomena . Privacy concerns may arise from sensor fidelity and precision as well as correlation of data from multiple sensors. A single sensor’s output might not be sensitive, but the combination from two or more may raise privacy concerns .
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Modern systems nowadays are able to track any objects across an area covered by cameras and sensors detecting unusual activities in a large dedicated area by combining information from different sources, as well as many objects as in video surveillance in crowded environ- ments in public areas, known as scene extraction techniques like the Google Street View technology which used to capture photos for use in Street view and may contain personal and sensitive information about any people who are unaware they are being observed and pho- tographed. Social media data can be used as an input source for scene extraction techniques. When these data are posted, however, users are unlikely to know that their data would be used in these aggregated ways and that their social media information, although public, might appear synthesized in new forms . Automated speech recognition has existed since 1950s approximately, but recent developments over the last 10 years have allowed for novel new capabilities like a spoken text such as news broadcasters reading part of a document can today be recognized with an accuracy of higher than 95% using state-of-the-art techniques. Spontaneous speech is much harder to recognize accurately. In recent years there has been a dramatic increase in the corpuses of spontaneous speech data available to researchers, which has allowed for improved accuracy . Over the next few years speech recognition interfaces will be in many more places, for example, multiple companies are exploring speech recogni- tion to control televisions, cars, find a show on TV, or to choose any object in Glass Corning’s vision technology. Google has already implemented some of this basic functionality in its Google Glass product, and Microsoft’s Xbox One system already integrates machine vision and multimicrophone audio input for controlling system functions .
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A point of interest is the hardware setting to use for ST-TOLAP. Distributed DBS’s (database-systems) suit best for distributed al- lies being part of the workflow while parallel DBS’s are made for massive OLTP where several DBS servers host a copy of the same DBS to provide massive multi-user usage or for OLAP to solve one complex analytic query in parallel on one large data set distributed on a cluster as mentoined in common computer science literature. A Cluster usually is made of high speed con- nected racks consisting of high speed connected blades which are basic computers with a HDD (Hard Disk Drive) or a SSD(Solid State Drive), RAM (Random Access Memory) and a CPU (Cen- tral Processing Unit) in case of a Shared-Nothing-System. In case of a Shared-Disk-System the blades share a number of HDDs or SSDs. High efficiency is expected by Shared-Nothing-Systems if the data is well distributed such that every blade has average work load to do for nearly all transaction/processe-types. Shared- Disk-Systems are not as dependent on data distribution as Shared- Nothing-Systems but synchronization efforts could slow down
• Atau dalam arti lain, Hadoop adalah Software platform (platform perangkat lunak) sebagai analytic engine yang memungkinkan seseorang dengan mudah untuk melakukan pembuatan penulisan perintah (write) dan menjalankan (run) aplikasi yang memproses data dalam jumlah besar, dan di dalamnya terdiri dari:
Pemerintah sebagai pelayan masyarakat memiliki peran yang sangat besar dalam meningkatkan kesejahteraan masyarakat.Maka perlu diadakan suatu perbaikan secara bertahap guna meningkatkan pelayanan masyarakat (public services) sebagai tugas utama pemerintah, untuk itu perlu adanya sikap keterbukaan dari pemerintah untuk dapat menerima setiap keluhan masyarakat mengenai kebijakan / program yang langsung menyentuh kepentingan masyarakat. Media Center merupakan sistem pelayanan informasi yang terintegrasi kepada masyarakat Kota Surabaya untuk ikut berpartisipasi dalam pembangunan dengan berbagai cara seperti ide, pengaduan, keluhan, kritik, saran dan pertanyaan. Untuk itu perlu adanya klasifikasi untuk sentiment analysis keluhan masyarakat informasi yang masuk ke media center sehingga pengelola dapat memberikan informasi yang efisien dan tepat kepada masyarakat dan pemerintah dapat mengetahui bidang mana yang perlu dibenahi dalam pembangunan. Sentiment analysis merupakan proses klasifikasi dokumen tekstual ke dalam beberapa kelas seperti sentimen positif dan negatif serta besarnya pengaruh dan manfaat dari sentiment analysis tersebut. Pada penelitian ini dibahas klasifikasi keluhan masyarakat terhadap pemerintah pada media sosial facebook dan twitter sapawarga data berbahasa Indonesia menggunakan metode Support Vector Machine (SVM) yang dijalankan dalam komputasi terdistribusi dengan menggunakan Hadoop. Pengujian dilakukan dengan perhitungan precision, kecepatan, akurasi.Hal ini bertujuan untuk mengetahui sejauh mana kehandalan metode yang diusulkan untuk mencapai peningkatan kecepatan dan akurasi klasifikasi.
Any peace process is an exercise in the negoti- ation of big data. From centuries old commu- nal hagiography to the reams of official texts, media coverage and social media updates, peace negotiations generate data. Peacebuild- ing and peacekeeping today are informed by, often respond and contribute to big data. This is no easy task. As recently as a few years ago, before the term big data embraced the virtual on the web, what informed peace process design and implementation was in the physi- cal domain – from contested borders and resources to background information in the form of text. The move from analogue, face- to-face negotiations to online, asynchronous, web-mediated negotiations – which can still include real world meetings – has profound implications for how peace is strengthened in fragile democracies.
are gaps in a complete, or true end-to-end supply chain dataflow model for most respondents. For example, only about half of survey respondents report that logistics, distributors, and minor direct suppliers are part of their supply chain dataflow. True end-to-end supply chain dataflow, including suppliers’ suppliers and customers’ customers, remains a challenge. Supply chain and operations management professionals have more work to do in terms of improving tools, technologies, strategies, and relationships, and big data can play a major role in this progress.
Although security and privacy principles for traditional data can be applied to big data, hence many of the existing security technologies and best practices can be extended to the big data ecosystem, the different characteristics of big data require modified approaches to meet the new challenges to effective big data management. There are several areas in which big data faces different or higher risk than tra- ditional data. Higher volume translates into higher risk of exposure when security breach occurs. More variety means new types of data and more complex security measure. Increased data velocity implies added pressure for security measures to keep up with the dynamics and faster response/recovery time. Most organizations are just starting to embrace big data, thus the big data governance and security policy are likely not at a high level of maturity, which is also a reflection of the immaturity of the big data industry and bid data ecosystem.
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.
HPC is being used to run computer simulations that model components of a product before the manufacturing process begins. Engineers from Bentley Motors used one such system to create virtual models of vehicles. This enabled faster product development times, decreased the number of prototypes required, reduced costs and eliminated the need for late-stage modification. Design decisions for some new products are being influenced more by data analytics. For example, the ability to monitor social media activity and analyse it in real-time enables companies to gain insight into customer response to a new or proposed product almost immediately. Ford used data from sales and social media to decide the design features on one of its new cars.
While some big data datasets are unidimensional or single channel, focusing, for example, on a par- ticular transaction or communication behavior and relying on single-channel interactions (e.g., via phone or email), there are increasingly opportuni- ties to collect and analyze multidimensional data- sets that offer insight into constellations of behav- iors, often through a variety of channels (e.g., call center customer interactions that switch between voice, web, chat, mobile, video, etc). For manage- ment researchers, the result of such richness is that there are unprecedented opportunities to notice po- tentially important variables that previous studies might have failed to consider at all, due to their necessarily more focused nature. And, once such variables capture a researcher’s attention, the rela- tionships between them can be explored and the contextual conditions under which these relation- ships may or may not hold can be examined.
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.
Security wise, companies that store big data needs to have robust information security systems since they too are constantly targeted by hackers. The big data collector needs to be regulated and audit as to what the collects and what efforts they have in place to secure data storedinn their databases. That means, collectors will be encouraged through incentive policies to stay proactive by constantly looking for threats to their system. Big data in itself can help do this task.