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""Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators. . Learn general methods for specifying Big Data in a way that is understandable to humans and to computers. . Avoid the pitfalls in Big D"

"Current developments in EU internal security policy, such as increased information sharing (which includes the exchange of personal data between European law enforcement agencies and judicial actors in the area of freedom, security and justice (Europol, Eurojust, Frontex and OLAF)) and the access of EU agencies, in particular Europol and Eurojust, to data stored in European information systems such as the SIS (II), VIS, CIS or Eurodac raise interesting questions regarding the balance between the rights of individuals and security interests. This book deals with the complexity of the relations between these actors and offers for the first time a comprehensive overview of the structures for information exchange in the area of freedom, security and justice and their compliance with data protection rules in this field."

"Ekstraksi informasi merupakan sebuah tahap awal dari proses analisis data tekstual. Ekstraksi informasi diperlukan untuk mendapatkan informasi dari data tekstual sehingga dapat digunakan untuk proses analisis seperti misalnya klasifikasi dan kategorisasi. Data tekstualsangat dipengaruhi oleh bahasa, jika sebuah data tekstual berbahasa Arab maka karakter yang digunakan adalah karakter arab.Knowledge dictionary merupakan sebuah kamus yang dapat digunakan untuk mengekstraksi informasi dari data tekstual. Informasi yang diekstraksi menggunakan knowledge dictionary adalah konsep.Knowledge dictionary biasanya dibangun secara manual oleh seorang pakar yang tentunya membutuhkan waktu yang lama dan spesifik untuksetiap masalah. Pada penelitian ini diusulkan sebuah metode untuk membangun knowledge dictionary secara otomatis. Pembentukanknowledge dictionary dilakukan dengan cara mengelompokkan kalimat yang memiliki konsep yang sama, dengan asumsi kalimat yang memiliki konsep yang sama akan memiliki nilai simi laritas yang tinggi. Konsep yang telah diekstraksi dapat digunakan sebagai fitur untuk proses komputasi berikutnya misalnya klasifikasi ataupun kategorisasi.Dataset yang digunakan dalam penelitian ini adalah dataset teks Arab. Hasil ekstraksi diuji dengan menggunakan mesin klasifikasidecision tree dan didapatkan nilai presisi tertinggi 71,0% dan nilai recall tertinggi 75,0%.

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AbstractInformation extraction is an early stage of a process of textual data analysis. Information extraction is required to get information from textual data that can be used for process analysis, such as classification and categorization. A textual data is strongly influenced by the language. Arabic is gaining a significant attention inmany studies because Arabic language is very different from others, and in contrast to other languages, tools and research on the Arabic language is still lacking. The information extracted using the knowledgedictionary is a concept of expression. A knowledge dictionary is usually constructed manually by an expert and this would take a long time and is specific to a problem only. This paper proposed a method for automatically building a knowledge dictionary. Dictionary knowledge is formed by classifying sentences having the same concept, assuming that they will have a high similarity value. The concept that has been extracted can be used as features for subsequent computational process such as classification or categorization. Dataset used in this paper was the Arabic text dataset. Extraction result was tested by using a decision tree classification engine and the highest precision value obtained was 71.0% while the highest recall value was 75.0%. "

"ABSTRAKThe paper investigates both the causes and effects of the rapid increase in the data volume Big Data and their impact on human cognition. The role of the Internet in distributing and exchanging of such data, and their impact on the growth of the Information Society are emphasized. As a result, Wisdom Science a new kind of research emerges which has the potential to facilitate more advanced solutions in the digital world. In consequence, new kinds of info driven devices, services and systems called smart are developed and applied in almost every aspect of human activities around the world. However, this is not enough for humans to use all those well informed smart devices and systems because, first of all, their decisions should be wise. Therefore, the paper, coming from a cognitive informatics approach defines wisdom and its applications, illustrated by some practical cases. Based on this, relations between knowledgeand wisdom are shown, and human abilities corresponding to them are defined. They can decide about a transformation of a knowledge society to a wisdom society."

"Principles of big data helps readers avoid the common mistakes that endanger all big data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to big data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate big data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from big data resources can be used for purposes beyond those imagined by the data creators."

"Badan Pusat Statistik (BPS) merupakan lembaga pemerintahan yang bertugas melakukan kegiatan statistik berupa sensus dan survei untuk menyediakan kebutuhan data bagi pemerintah, masyarakat dan pihak swasta. Untuk mengoptimalkan proses bisnis utama BPS dalam memproduksi data dan memberikan layanan data kepada pengguna dibutuhkan ketersediaan infrastruktur dan layanan teknologi informasi (TI) yang berkualitas. Semua layanan TI di lingkungan BPS diharapkan dapat diberikan sesuai dengan target yang telah disepakati dalam Service Level Agreements (SLA). Namun kenyataannya, pada laporan yang didapatkan dari sistem informasi pusat layanan TI BPS, terdapat beberapa layanan TI yang waktu penyelesaiannya melebihi jatuh tempo atau target yang sudah ditetapkan. Permasalahan ini disebabkan karena kurangnya tingkat knowledge sharing pegawai dalam memberikan layanan TI. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi knowledge sharing pegawai, serta memberikan rekomendasi untuk meningkatkan knowledge sharing pegawai dalam layanan TI di Direktorat Sistem Informasi Statistik (SIS) BPS. Kerangka teoretis yang dirancang pada penelitian ini mengadopsi dari penelitian-penelitian sebelumnya dengan menggunakan dimensi individu, organisasi, dan teknologi. Penelitian ini merupakan penelitian studi kasus, survey research dengan pendekatan metode kuantitatif dan metode kualitatif untuk menyusun rekomendasi peningkatan knowledge sharing pegawai. Pengumpulan data dilakukan melalui survei dan analisis data dalam penelitian ini menggunakan teknik Partial Least Squares Structured Equation Model (PLS-SEM). Berdasarkan hasil analisis, faktor-faktor yang terbukti berpengaruh terhadap kemampuan pegawai dalam melakukan knowledge sharing di Direktorat SIS adalah self efficacy, personal expectation, social capital, IT utilization dan end user focus.

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Badan Pusat Statistik (Central Bureau of Statistics) is non-departmental government institute of Indonesia that is responsible for conducting a statistical survey to provide data needs for governments, communities and private parties. Quality infrastructure and information technology (IT) services are needed to optimize BPS main business processes in producing data and providing data services to users.

All IT services in the BPS environment are expected to be provided by targets agreed upon in Service Level Agreements (SLAs). However, in reality, the reports obtained from IT service centre information system contained IT services whose completion time exceeded the predetermined time target. This problem is caused by the lack of level of employee knowledge sharing in providing IT services.

This study aims to analyze the factors that influence employee knowledge sharing and also provide recommendations for improving employee knowledge sharing capabilities in IT services at the Directorate of Statistical Information Systems (SIS) BPS. The theoretical framework designed in this study adopts from previous studies using individual, organizational, and technological dimensions.

This research is a case study research, survey research with quantitative method approach and qualitative method to develop recommendations for increasing employee knowledge sharing capabilities. Data collection was carried out through surveys and analyzed by Partial Least Squares Structured Equation Model (PLS-SEM) techniques.

Based on the results of the analysis, the factors that proved to influence the ability of employees to carry out knowledge sharing were self-efficacy, personal expectation, social capital, IT utilization and end-user focus."