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"This book discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management."

"E-commerce dan big data merupakan bukti dari kemajuan teknologi yang sangat pesat. Big data berperan cukup penting dalam perusahaan e-commerce untuk menangani perkembangan semua data, mengolah setiap data tersebut dan menjadi competitive advantage bagi perusahaan. Perusahaan XYZ.com mengalami kesulitan dalam menganalisis stok dan tren dari produk yang dijual. Jika hal ini tidak ditanggulangi, maka perusahaan XYZ.com akan kehilangan opportunity gain. Untuk menentukan tren dan stok produk secara cepat dengan akurat, dibutuhkan big data predictive analysis. Penelitian ini mengolah data transaksi menjadi data yang dapat dianalisis untuk menentukan tren dan prediksi tren produk berdasarkan kategorinya dengan menggunakan big data predictive analysis. Hasil dari penelitian ini akan memberikan informasi kepada pihak manajemen kategori apa yang berpotensi menjadi tren dan jumlah minimal stok yang harus disediakan dari kategori produk tersebut.

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E commerce and big data are evidence of rapid technological advances. Big data plays an important role in e commerce companies to handle and analyze all data changes, and become a competitive advantage for the company. XYZ.com experience a difficulty in analyzing stocks and commerce product trend. If this issue not addressed, XYZ.com company will lose an opportunity gain. To determine trends and stock accurately, XYZ.com can use big data predictive analysis. This study processes transaction data into data that can be analyzed to determine trends and predictions of product trends based on its categories using big data predictive analysis. The results of this study give massive informations to management about what categories will potential become trends and minimum stock required to be provided."

"Access to big data, the “new commodity” for the 21st century economies, and its uses and potential abuses, has both conceptual and methodological impacts for the field of comparative and international education. This book examines, from a comparative perspective, the impact of the movement from the so-called knowledge-based economy towards the Intelligent Economy, which is premised upon the application of knowledge. Knowledge, the central component of the knowledge-based economy, is becoming less important in an era that is projected to be dominated and defined by the integration of complex technologies under the banner of the fourth industrial revolution. In this new era that blends the physical with the cyber-physical, the rise of education intelligence means that clients including countries, organizations, and other stakeholders are equipped with cutting-edge data in the form of predicative analytics, and knowledge about global educational predictions of future outcomes and trends. In this sense, this timely volume links the advent of this new technological revolution to the world of governance and policy formulation in education in order to open a broader discussion about the systemic and human implications for education of the emerging intelligent economy. By providing a unique comparative perspective on the Educational Intelligent economy, this book will prove invaluable for researchers and scholars in the areas of comparative education, artificial intelligence and educational policy."

"Privasi data menjadi perhatian krusial dalam lanskap bisnis saat ini, terutama dengan Big Data dan Analytics (BD&A) serta kecerdasan buatan (AI). Diulas melalui empat artikel, lanskap analitika bisnis yang terus berkembang membahas aspek sejarah, tantangan implementasi, dan perannya yang transformatif. Sambil menyoroti manfaat BD&A dan AI, esai menekankan kebutuhan mendesak akan kesadaran dan langkah-langkah proaktif untuk mengatasi isu privasi data. Esai ini menekankan dampak negatif dari pengumpulan data yang luas dan menganjurkan perlindungan informasi pribadi melalui regulasi yang ketat. Diskusinya menekankan kesiapan organisasi dan pengembangan kepemimpinan untuk mengatasi tantangan dalam adopsi BD&A sambil memastikan perlindungan data yang sensitif. Esai ini menyimpulkan dengan mengajak untuk lebih mendalami privasi data melalui studi kasus di masa depan untuk mengurangi risiko dalam penanganan informasi rahasia di lingkungan digital yang dinamis.

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Data privacy is a critical concern in today's business landscape, particularly with Big Data and Analytics (BD&A) and artificial intelligence (AI). Explored through four articles, the evolving business analytics landscape addresses historical aspects, implementation challenges, and its transformative role. While highlighting the benefits of BD&A and AI, the essay emphasizes the urgent need for awareness and proactive measures to address data privacy issues. It underscores the drawbacks of extensive data collection and advocates for safeguarding personal information through stringent regulations. The discussion stresses organizational readiness and leadership development to navigate challenges in BD&A adoption while ensuring sensitive data protection. The essay concludes by calling for deeper exploration of data privacy in future case studies to mitigate risks in handling confidential information in the dynamic digital environment."

"Provides an introduction to the fundamental principles of data science, walking the reader through the "data-analytic thinking" necessary for extracting useful knowledge and business value from collected data."

"ABSTRAKPada tahun 2010, seluruh data di dunia mencapai 1 ZB (Zetabyte), dan pada tahun 2014, diprediksi akan mencapai 72 ZB. Sebagian besar dari data-data tersebut seringkali ditemukan dalam bentuk tidak teratur, berukuran besar dan mengalir dengan cepat. Data dengan karakteristik tersebut dikenal dengan sebutan Big Data. PT Indosat melihat potensi yang ada dalam Big Data untuk mendukung pengembangan bisnis yang ada. Penelitian ini melakukan justifikasi rencana investasi Big Data dengan melakukan identifikasi manfaat investasi menggunakan Tabel Manfaat Bisnis SI/TI Generik.System Dynamics digunakan untuk menyederhanakan manfaat dan mensimulasikan model. Identifikasi perubahan proses bisnis dilakukan menggunakan pendekatan perubahan arsitektur bisnis, data/informasi dan aplikasi menggunakan TOGAF ADM, Frameworx dan Benefit Dependency Network. Manfaat bisnis yang diperoleh akan dikuantifikasi untuk mencari nilai finansialnya serta diidentifikasi potensi dan indikator risikonya. Potensi risiko dan indikatornya diidentifikasi menggunakan kerangka COSO ERM.Hasil dari penelitian ini didapat manfaat bisnis yang dapat diraih melalui investasi Big Data. Manfaat tersebut adalah peningkatan pendapatan dari kepercayaan pelanggan (IRE-03) dan pengurangan risiko biaya kehilangan (ACO-03). Potensi risiko dan indikator risiko yang teridentifikasi dapat dikelompokkan pada area teknologi, sumber daya manusia, dan pelayanan pelanggan. Perubahan bisnis proses digambarkan melalui diagram Benefit Dependency Network.

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ABSTRACT

In year 2010, data volume around the world reached 1 zetabyte and in 2014, predicted will increase to 72 zetabyte. Most of the data are in unstructured format, huge volume and high velocity in streaming. Data with these characteristics are known as Big Data. PT Indosat see the potential of Big Data to support their business growth. In this research, business benefit identification will be performed using Ranti?s Generic IS/IT Business Benefit Table.

System dynamics will be used to simplify identified business benefits and to run simulation on the model. Business process changes identification will be performed by analyzing changes on business, data/information and application architecture using TOGAF ADM, Frameworx and Benefit Dependency Network. Identified business benefits will be quantified for financial value. Potential risks and its indicators will also be identified using COSO ERM framework.

From this research, can be concluded that business benefits from investment plan on Big Data are increase revenue from customer trust (IRE-03) and avoiding cost of lost and delay cost (ACO-03). Identified potential risks and its indicators are group into several area, which are technology, human resources and customer service. Bussines process changes is depicted using Benefit Dependency Network diagram."

"Data mining is one of the most rapidly growing research areas in computer science and statistics. Areas of application covered are diverse and include healthcare and finance. We wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field."

"Time series big data dynamically changes the size, and, unfortunately, it may be difficult to curate the enormous amount of data due to the processing capacity and storage size. This big data allows researcher to iterate on the model millions of times over. To execute a regression on several billion rows of data on a distributed network, the resource capacity regarding large volumes of data and its distributed environment must be considered. Algorithms must be real-time based data awareness. Moreover, analyzing big data sources requires the data to be pre-processed rather than immediately collected and analyzed. This pre-processing approach for the big data sources helps minimize the amount of collected data by extracting insights. It analyzes big data quicker and is cost-effective for storage space. Hence, in this research, an approximation method for analyzing regression problems in a big data stream with parallelism is proposed. The partitioning method for huge data stream helps reduce the computing time and required space, and the speed-up can improve the processing time. The performance evaluation of concurrent regression model is first executed by massive online analysis (MOA) simulation. Then, to validate the approximation method, the results performed by our proposed method are compared to those results collected from the simulation. The comparisons show evenly between the two methods."