The OTree: multidimensional indexing with efficient data sampling for HPC

Spatial big data is considered an essential trend in future scientific and business applications. Indeed, research instruments, medical devices, and social networks generate hundreds of petabytes of spatial data per year. However, many authors have pointed out that the lack of specialized frameworks for multidimensional Big Data is limiting possible applications and precluding many scientific breakthroughs. Paramount in achieving High-Performance Data Analytics is to optimize and reduce the I/O operations required to analyze large data sets. To do so, we need to organize and index the data according to its multidimensional attributes. At the same time, to enable fast and interactive exploratory analysis, it is vital to generate approximate representations of large datasets efficiently. In this paper, we propose the Outlook Tree (or OTree), a novel Multidimensional Indexing with efficient data Sampling (MIS) algorithm. The OTree enables exploratory analysis of large multidimensional datasets with arbitrary precision, a vital missing feature in current distributed data management solutions. Our algorithm reduces the indexing overhead and achieves high performance even for write-intensive HPC applications. Indeed, we use the OTree to store the scientific results of a study on the efficiency of drug inhalers. Then we compare the OTree implementation on Apache Cassandra, named Qbeast, with PostgreSQL and plain storage. Lastly, we demonstrate that our proposal delivers better performance and scalability.Peer ReviewedPostprint (author's final draft

PENGEMBANGAN MEDIA PEMBELAJARAN INTERAKTIF BIG BLUE BUTTON PADA MATAKULIAH STRUKTUR DATA

The development of the Big Blue Button interactive learning media in the Data Structures course is an innovative step in the world of education. The aim of this research is to increase student learning effectiveness in understanding basic concepts in the Data Structures course. This research focuses on the design and development of Big Blue Button-based learning media with various interactive features contained in it. The main aim of this research is to create a learning environment that supports discussion, interaction, presentation and collaboration between lecturers and students. The research methods used include requirements analysis, interface design, development and testing. The research results show that the use of the Big Blue Button in the Data Structures course can improve the quality of learning, increase student participation, and make it easier to access learning materials. In addition, educational institutions can utilize web conferencing technology to enrich student learning experiences. This research is expected to have the potential to provide significant benefits in distance learning, especially in the context of learning programming techniques. By using this technology students can gain a more interactive and collaborative learning experience in understanding data structure concepts. Keyword: Interactive Learning Media, Big Blue Buttons, Data StructuresPengembangan media pembelajaran interaktif Big Blue Button pada matakuliah Struktur Data ini merupakan suatu langkah inovatif dalam dunia pendidikan. Adapun tujuan dari penelitian ini yaitu untuk meningkatkan efektivitas pembelajaran mahasiswa dalam memahami konsep-konsep dasar pada matakuliah Struktur Data. Penelitin ini berfokus pada perancangan dan pengembangan media pembelajaran berbasis Big Blue Button dengan berbagai fitur interaktif yang ada didalamnya. Tujuan utama dari penelitian ini untuk menciptakan lingkungan pembelajaran yang mendukung diskusi, interaktif, presentasi, dan kolaborasi antara dosen dan mahasiswa. Metode penelitian yang dipergunakan mencakup analisis kebutuhan, perancangan antarmuka, pengembangan, dan uji coba. Hasil dari penelitian ini menunjukkan bahwa penggunaan Big Blue Button pada matakuliah Struktur Data dapat meningkatkan kualitas pembelajaran, meningkatkan partisipasi mahasiswa, dan memudahkan akses ke materi pembelajaran. Selain itu, bagi institusi pendidikan dalam memanfaatkan teknologi web conferencing untuk memperkaya pengalaman pembelajaran mahasiswa. Harapannya penelitian ini berpotensi memberikan manfaat yang signifikan  dalam pembelajaran jarak jauh terutama dalam konteks pembelajaran teknik pemrograman. Dengan menggunakan teknologi ini mahasiswa dapat memiliki pengalaman pembelajaran yang lebih interaktif dan kolaboratif dalam memahami konsep struktur data

Big data and Parkinson’s: Exploration, analyses, data challenges and visualization

In healthcare, a tremendous amount of clinical, laboratory tests, imaging, prescription and medication data are collected. Big data analytics on these data aim at early detection of disease which will help in developing preventive measures and in improving patient care. Parkinson disease is the second-most common neurodegenerative disorder in the United States. To find a cure for Parkinson\u27s disease biological, clinical and behavioral data of different cohorts are collected, managed and propagated through Parkinson’s Progression Markers Initiative (PPMI). Applying big data technology to this data will lead to the identification of the potential biomarkers of Parkinson’s disease. Data collected in human clinical studies is imbalanced, heterogeneous, incongruent and sparse. This study focuses on the ways to overcome the challenges offered by PPMI data which is wide and incongruent. This work leverages the initial discoveries made through descriptive studies of various attributes. The exploration of data led to identifying the significant attributes. This research project focuses on data munging or data wrangling, creating the structural metadata, curating the data, imputing the missing values, using the emerging big data analysis methods of dimensionality reduction, supervised machine learning on the reduced dimensions dataset, and finally an interactive visualization. The simple interactive visualization platform will abstract the domain expertise from the sophisticated mathematics and will enable a democratization of the exploration process. Visualization build on D3.Js is interactive and will enable manual exploration of traits that correlate with the disease severity

New Era, New Opportunity, Is GES DISC Ready for Big Data Challenge?

The new era of Big Data has opened doors for many new opportunities, as well as new challenges, for both Earth science research/application and data communities. As one of the twelve NASA data centers - Goddard Earth Sciences Data and Information Services Center (GES DISC), one of our great challenges has been how to help research/application community efficiently (quickly and properly) accessing, visualizing and analyzing the massive and diverse data in natural hazard research, management, or even prediction. GES DISC has archived over 2000 TB data on premises and distributed over 23,000 TB of data since 2010. Our data has been widely used in every phase of natural hazard management and research, i.e. long term risk assessment and reduction, forecasting and predicting, monitoring and detection, early warning, damage assessment and response. The big data challenge is not just about data storage, but also about data discoverability and accessibility, and even more, about data migration/mirroring in the cloud. This paper is going to demonstrate GES DISCs efforts and approaches of evolving our overall Web services and powerful Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) tool into further improving data discoverability and accessibility. Prototype works will also be presented

PENGEMBANGAN E-LKPD INTERAKTIF BERORIENTASI MODEL PEMBELAJARAN FLIPPED CLASSROOM PADA PEMBELAJARAN IPA SISWA KELAS V SD NEGERI 67 PALEMBANG

This study aims to develop an Interactive E-LKPD Oriented Flipped Classroom Learning Model in the human digestive organs that is valid, practical and effective. This research is a type of Research and Development (R&D) research. This study uses the ADDIE model which consists of (Analysis, Design, Development, Implementation, and Evaluation). Validity analysis, practicality analysis, and effectiveness analysis are the three data analysis techniques employed. Three experts, including media experts, material experts, and language experts, carried out the validation analysis procedure; on average, 86.2% of the results were deemed to be extremely genuine. By putting it through a process of practicality analysis on eight students in a small group trial stage, it was found to be practical with an average score of 72.18%. Students in one class of 22 were assessed in big groups (big Group) to evaluate the efficacy of the process of measuring the effectiveness, and they received an average N Gain Score of 60%. characterized as efficient. Thus, it can be said that the interactive E-LKPD that is targeted toward flipped classrooms is extremely valid, applicable, and effective. This interactive E-LKPD that is geared at the flipped classroom may be utilized to aid in learning. Keywords: ADDIE, E-LKPD, Flipped Classroom, Human Digestive Organ

PENGEMBANGAN E-LKPD INTERAKTIF BERORIENTASI MODEL PEMBELAJARAN FLIPPED CLASSROOM PADA PEMBELAJARAN IPA SISWA KELAS V SD NEGERI 67 PALEMBANG

This study aims to develop an Interactive E-LKPD Oriented Flipped Classroom Learning Model in the human digestive organs that is valid, practical and effective. This research is a type of Research and Development (R&D) research. This study uses the ADDIE model which consists of (Analysis, Design, Development, Implementation, and Evaluation). Validity analysis, practicality analysis, and effectiveness analysis are the three data analysis techniques employed. Three experts, including media experts, material experts, and language experts, carried out the validation analysis procedure; on average, 86.2% of the results were deemed to be extremely genuine. By putting it through a process of practicality analysis on eight students in a small group trial stage, it was found to be practical with an average score of 72.18%. Students in one class of 22 were assessed in big groups (big Group) to evaluate the efficacy of the process of measuring the effectiveness, and they received an average N Gain Score of 60%. characterized as efficient. Thus, it can be said that the interactive E-LKPD that is targeted toward flipped classrooms is extremely valid, applicable, and effective. This interactive E-LKPD that is geared at the flipped classroom may be utilized to aid in learning. Keywords: ADDIE, E-LKPD, Flipped Classroom, Human Digestive Organ

GiViP: A Visual Profiler for Distributed Graph Processing Systems

Analyzing large-scale graphs provides valuable insights in different application scenarios. While many graph processing systems working on top of distributed infrastructures have been proposed to deal with big graphs, the tasks of profiling and debugging their massive computations remain time consuming and error-prone. This paper presents GiViP, a visual profiler for distributed graph processing systems based on a Pregel-like computation model. GiViP captures the huge amount of messages exchanged throughout a computation and provides an interactive user interface for the visual analysis of the collected data. We show how to take advantage of GiViP to detect anomalies related to the computation and to the infrastructure, such as slow computing units and anomalous message patterns.Comment: Appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017

GPU Accelerated Browser for Neuroimaging Genomics

Neuroimaging genomics is an emerging field that provides exciting opportunities to understand the genetic basis of brain structure and function. The unprecedented scale and complexity of the imaging and genomics data, however, have presented critical computational bottlenecks. In this work we present our initial efforts towards building an interactive visual exploratory system for mining big data in neuroimaging genomics. A GPU accelerated browsing tool for neuroimaging genomics is created that implements the ANOVA algorithm for single nucleotide polymorphism (SNP) based analysis and the VEGAS algorithm for gene-based analysis, and executes them at interactive rates. The ANOVA algorithm is 110 times faster than the 4-core OpenMP version, while the VEGAS algorithm is 375 times faster than its 4-core OpenMP counter part. This approach lays a solid foundation for researchers to address the challenges of mining large-scale imaging genomics datasets via interactive visual exploration

Discrete event simulation and virtual reality use in industry: new opportunities and future trends

This paper reviews the area of combined discrete event simulation (DES) and virtual reality (VR) use within industry. While establishing a state of the art for progress in this area, this paper makes the case for VR DES as the vehicle of choice for complex data analysis through interactive simulation models, highlighting both its advantages and current limitations. This paper reviews active research topics such as VR and DES real-time integration, communication protocols, system design considerations, model validation, and applications of VR and DES. While summarizing future research directions for this technology combination, the case is made for smart factory adoption of VR DES as a new platform for scenario testing and decision making. It is put that in order for VR DES to fully meet the visualization requirements of both Industry 4.0 and Industrial Internet visions of digital manufacturing, further research is required in the areas of lower latency image processing, DES delivery as a service, gesture recognition for VR DES interaction, and linkage of DES to real-time data streams and Big Data sets