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"Topics covered include tools and services for creating simple, rich, and reusable knowledge representations to explore strategies for integrating this knowledge into legacy systems. The reuse and integration are essential concepts that must be enforced to avoid duplicating the effort and reinventing the wheel each time in the same field. This problem is investigated from different perspectives. This book helps readers to maximize the reuse of information. "

"[Data fusion in information retrieval This book offers a theoretical and empirical approach to data fusion, used in information retrieval in complex, diverse settings such as web and social networks, legal, enterprise and others. Discusses, analyzes and ealuates typical data fusion algorithms., Data fusion in information retrieval This book offers a theoretical and empirical approach to data fusion, used in information retrieval in complex, diverse settings such as web and social networks, legal, enterprise and others. Discusses, analyzes and ealuates typical data fusion algorithms.]"

"Managing data in motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment.The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired.The management of the "data in motion" in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and "big data" applications are just a few of the challenges facing organizations and businesses today. Managing data in motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects."

"Universitas Terbuka (UT) menyediakan student support services untuk meningkatkan hasil pembelajaran mahasiwa dan persistensi mahasiswa untuk tetap menyelesaikan studinya di Pendidikan Jarak Jauh. Namun, fakta lapangan menunjukkan rata-rata IPK dan IPS mahasiswa Sarjana dan Diploma Angkatan 20161 s/d 20182 masih di bawah standar IPK tuntutan pasar kerja (2.75). Solusi permasalahan tersebut adalah mendeteksi dini mahasiswa berisiko gagal menggunakan metode klasifikasi data mining berdasarkan data aktivitas Tutorial Online (Tuton) dan data pribadi mahasiswa. Pengklasifikasian mahasiswa berisiko gagal berdasarkan nilai IPS agar dapat mendeteksi lebih awal tidak hanya di semester awal tetapi juga di semester berikutnya. Selain itu, nilai IPS memiliki korelasi positif yang kuat terhadap nilai IPK sehingga nilai IPS dianggap dapat sebagai indikasi awal dari risiko kegagalan. Algoritma klasifikasi untuk model deteksi dini mahasiswa berisiko menggunakan naïve bayes, logistic regression, SVM, decision tree (CART, C5.0), random forest, dan adaboost. Tahap awal pengujian model menggunakan data aktivitas Tuton masa 20182-20191. Pembagian data training dan data testing menggunakan Stratified K-fold sebanyak 10 kali iterasi dan melakukan eksperimen metode tanpa sampling class imbalance dan metode random undersampling (50P:50N, 70P:30P, 66P:33P, 60P:40N) pada data training. Pada tahap awal pengujian model menunjukkan F1-score di minggu ke-empat tidak berbeda signifikan dengan minggu ke-delapan sehingga dianggap sebagai waktu yang tepat untuk mengintervensi lebih awal agar mahasiswa dapat berjuang di tugas berikutnya. F1-score tertinggi dari tahap awal pengujian model adalah tanpa sampling class imbalance di data training dengan algoritma random forest (90.20%), adaboost (89.20%), dan decision tree CART (88.10%). Ketiga algoritma terbaik akan diuji kembali pada tahap akhir menggunakan data testing aktivitas Tuton masa 20192. Hasil tahap akhir kinerja model deteksi dini mahasiswa berisiko kegagalan berdasarkan F1-score menunjukkan algoritma adaboost dengan nilai tertinggi (84.7%) diikuti oleh algoritma random forest (83.8%). Berdasarkan pengukuran recall, CART menunjukkan nilai tertinggi (99.9%) tetapi mengalami overfitting terhadap kelas positif sehingga tidak lebih baik dibandingkan melakukan intervensi ke seluruh mahasiswa. Kinerja terbaik untuk model deteksi dini mahasiswa berisiko gagal di UT adalah menggunakan algoritma adaboost.

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Universitas Terbuka (UT) provides student support services to improve student academic outcomes and student persistence for their completion in Distance Education. However, the average cumulative and semester GPA of Bachelor and Diploma programs from academic year 20161-20182 show below labor market standard GPA (2.75%). Solution to this problem is early detection on academic failure risk through the implementation of classification data mining to predict student at-risk academic failure using Online Tutorial (Tuton) activities data and student’s personal information. Classification student at-risk academic failure based on their semester GPA in order to early detect not only on the initial semester but also on the following semester. Furthermore, semester GPA has a strong positive correlation to cumulative GPA so that semester GPA is considered as an early indication of the risk of failure. The classification algorithm for student at-risk failure early detection model using naïve bayes, logistic regression, SVM, decision tree (CART, C5.0), random forest, and adaboost. The initial model testing stage use data from Tuton activities on 20182-20191. Splitting method of training data set and testing data set using Stratified K-fold in 10 times iteration and experimenting without class imbalance sampling and random undersampling method (50P:50N, 70P:30P, 66P:33P, 60P:40N) on training data set. On The initial model testing stage shows that F1-scores on fourth week are not significantly different from the eighth week so early intervention on fourth week is the right time for student to study harder on the next assignments. The highest F1-score from the initial model testing stage is without sampling imbalance on training data set using random forest (90.20%), adaboost (89.20%), and decision tree CART (88.10%). The three best algorithms will be tested again on the final testing stage using Tuton activity on 20192 as testing data set. The F1-score results on the final student at-risk of failure early detection model stage shows that adaboost algorithm highest performance (84.7%) and followed by random forest (83.8%). Based on recall results, CART showed the highest performance (99.9%) but tend to positive class overfitting so that it was no better than intervening all of students. The best performance for student at-risk of failure early detection models at UT is using adaboost algorithm."

"[As urban congestion continues to be an ever increasing problem, routing in these settings has become an important area of operations research. This monograph provides cutting-edge research, utilizing the recent advances in technology, to quantify the value of dynamic, time-dependent information for advanced vehicle routing in city logistics. The methodology of traffic data collection is enhanced by GPS based data collection, resulting in a comprehensive number of travel time records. Data Mining is also applied to derive dynamic information models as required by time-dependent optimization. Finally, well-known approaches of vehicle routing are adapted in order to handle dynamic information models. This book interweaves the usually distinct areas of traffic data collection, information retrieval and time-dependent optimization by an integrated methodological approach, which refers to synergies of Data Mining and Operations Research techniques by example of city logistics applications. These procedures will help improve the reliability of logistics services in congested urban areas.;As urban congestion continues to be an ever increasing problem, routing in these settings has become an important area of operations research. This monograph provides cutting-edge research, utilizing the recent advances in technology, to quantify the value of dynamic, time-dependent information for advanced vehicle routing in city logistics. The methodology of traffic data collection is enhanced by GPS based data collection, resulting in a comprehensive number of travel time records. Data Mining is also applied to derive dynamic information models as required by time-dependent optimization. Finally, well-known approaches of vehicle routing are adapted in order to handle dynamic information models. This book interweaves the usually distinct areas of traffic data collection, information retrieval and time-dependent optimization by an integrated methodological approach, which refers to synergies of Data Mining and Operations Research techniques by example of city logistics applications. These procedures will help improve the reliability of logistics services in congested urban areas., As urban congestion continues to be an ever increasing problem, routing in these settings has become an important area of operations research. This monograph provides cutting-edge research, utilizing the recent advances in technology, to quantify the value of dynamic, time-dependent information for advanced vehicle routing in city logistics. The methodology of traffic data collection is enhanced by GPS based data collection, resulting in a comprehensive number of travel time records. Data Mining is also applied to derive dynamic information models as required by time-dependent optimization. Finally, well-known approaches of vehicle routing are adapted in order to handle dynamic information models. This book interweaves the usually distinct areas of traffic data collection, information retrieval and time-dependent optimization by an integrated methodological approach, which refers to synergies of Data Mining and Operations Research techniques by example of city logistics applications. These procedures will help improve the reliability of logistics services in congested urban areas.]"