The Raincore API for clusters of networking elements

Clustering technology offers a way to increase overall reliability and performance of Internet information flow by strengthening one link in the chain without adding others. We have implemented this technology in a distributed computing architecture for network elements. The architecture, called Raincore, originated in the Reliable Array of Independent Nodes, or RAIN, research collaboration between the California Institute of Technology and the US National Aeronautics and Space Agency's Jet Propulsion Laboratory. The RAIN project focused on developing high-performance, fault-tolerant, portable clustering technology for spaceborne computing . The technology that emerged from this project became the basis for a spinoff company, Rainfinity, which has the exclusive intellectual property rights to the RAIN technology. The authors describe the Raincore conceptual architecture and distributed services, which are designed to make it easy for developers to port their applications to run on top of a cluster of networking elements. We include two applications: a Web server prototype that was part of the original RAIN research project and a commercial firewall cluster product from Rainfinity

Integration of IoT and chatbot for aquaculture with natural language processing

The development of internet of things (IoT) technology is very fast lately. One sector that can be implemented by IoT technology is the aquaculture sector. One important factor in the success of aquaculture is a good and controlled water quality condition. But the problem for the traditional aquaculture farmers is to monitor and increase the water quality quickly and efficiently. To resolve the above-mentioned problem, this paper proposes a real-time monitoring system for aquaculture and supported with chatbot assistant to facilitate the user. This system was composed of IoT system, cloud system, and chatbot system. The proposed system consists of 7 main modules: smart sensors, smart aeration system, local network system, cloud computing system, client visualization data, chatbot system, and solar powered system. The smart aeration system consists of NodeMCU, relay, and aerator. The smart sensors consist of several sensors such as dissolved oxygen, pH, temperature, and water level sensor. Natural language processing is implemented to build the chatbot system. By combining text mining processing with naive Bayes algorithm, the result shows the very good performance with high precision and recall for each class to monitor the quality of water in aquaculture sector

Online Monitoring Kualitas Air Pada Budidaya Udang Berbasis WSN Dan IoT

Dalam tulisan ini dijelaskan desain dan pengembangan sistem online monitoring kualitas air berbasis wireless sensor Network (WSN) dan Internet of Things (IoT). Sistem ini didesain dan dikembangkan untuk memantau parameter DO (Dissolved Oxygen), pH, conductivity dan temperatur pada budidaya udang. Sistem terdiri dari beberapa node sensor dengan komponen utama arduino uno yang terhubung dengan Xbee board dan master board dengan komponen utamanya adalah Raspberry Pi 2 (RPi2) board dan Xbee. Data dikirim dari masing-masing node ke RPi2 menggunakan jaringan WSN dengan paket data yang dilengkapi dengan masing-masing ID, setelah itu data disimpan di database internal RPi2 dan ditampilkan di graph. Timer update server digunakan untuk update data dari RPi2 ke server menggunakan jaringan internet melalui wifi. Data di server dapat dilihat menggunakan website, selain itu juga data dapat dilihat pada aplikasi Telegram Messenger yang ter-install di perangkat ponsel. Program RPi2 dikembangkan menggunakan bahasa python dan komponen matplotlib. Hasil percobaan menunjukkan bahwa sistem memiliki prospek yang besar dan dapat digunakan untuk keperluan budidaya udang dengan memberikan informasi yang relevan dan tepat waktu. Data hasil pengumpulan tersebut dapat digunakan untuk penelitian dan analisa lebih lanjut

Low-Bandwidth and Non-Compute Intensive Remote Identification of Microbes from Raw Sequencing Reads

Cheap high-throughput DNA sequencing may soon become routine not only for human genomes but also for practically anything requiring the identification of living organisms from their DNA: tracking of infectious agents, control of food products, bioreactors, or environmental samples. We propose a novel general approach to the analysis of sequencing data in which the reference genome does not have to be specified. Using a distributed architecture we are able to query a remote server for hints about what the reference might be, transferring a relatively small amount of data, and the hints can be used for more computationally-demanding work. Our system consists of a server with known reference DNA indexed, and a client with raw sequencing reads. The client sends a sample of unidentified reads, and in return receives a list of matching references known to the server. Sequences for the references can be retrieved and used for exhaustive computation on the reads, such as alignment. To demonstrate this approach we have implemented a web server, indexing tens of thousands of publicly available genomes and genomic regions from various organisms and returning lists of matching hits from query sequencing reads. We have also implemented two clients, one of them running in a web browser, in order to demonstrate that gigabytes of raw sequencing reads of unknown origin could be identified without the need to transfer a very large volume of data, and on modestly powered computing devices. A web access is available at http://tapir.cbs.dtu.dk. The source code for a python command-line client, a server, and supplementary data is available at http://bit.ly/1aURxkc

Development of Integrative Bioinformatics Applications using Cloud Computing resources and Knowledge Organization Systems (KOS).

Use of semantic web abstractions, in particular of domain neural Knowledge Organization Systems (KOS), to manage distributed, cloud based, integrative bioinformatics infrastructure. This presentation derives from recent publication:

Almeida JS, Deus HF, Maass W. (2010) S3DB core: a framework for RDF generation and management in bioinformatics infrastructures. BMC Bioinformatics. 2010 Jul 20;11(1):387. [PMID 20646315].

These PowerPoint slides were presented at Semantic Web Applications and Tools for Life Sciences December 10th, 2010, Berlin, Germany (http://www.swat4ls.org/2010/progr.php), keynote 9-10 am

Development of Integrative Bioinformatics Applications using Cloud Computing resources and Knowledge Organization Systems (KOS).

Use of semantic web abstractions, in particular of domain neural Knowledge Organization Systems (KOS), to manage distributed, cloud based, integrative bioinformatics infrastructure. This presentation derives from recent publication:

Almeida JS, Deus HF, Maass W. (2010) S3DB core: a framework for RDF generation and management in bioinformatics infrastructures. BMC Bioinformatics. 2010 Jul 20;11(1):387. [PMID 20646315].

These PowerPoint slides were presented at Semantic Web Applications and Tools for Life Sciences December 10th, 2010, Berlin, Germany (http://www.swat4ls.org/2010/progr.php), keynote 9-10 am

Online Monitoring Kualitas Air pada Budidaya Udang Berbasis WSN dan IoT

Dalam tulisan ini dijelaskan desain dan pengembangan sistem online monitoring kualitas air berbasis wireless sensor Network (WSN) dan Internet of Things (IoT). Sistem ini didesain dan dikembangkan untuk memantau parameter DO (Dissolved Oxygen), pH, conductivity dan temperatur pada budidaya udang. Sistem terdiri dari beberapa node sensor dengan komponen utama arduino uno yang terhubung dengan Xbee board dan master board dengan komponen utamanya adalah Raspberry Pi 2 (RPi2) board dan Xbee. Data dikirim dari masing-masing node ke RPi2 menggunakan jaringan WSN dengan paket data yang dilengkapi dengan masing-masing ID, setelah itu data disimpan di database internal RPi2 dan ditampilkan di graph. Timer update server digunakan untuk update data dari RPi2 ke server menggunakan jaringan internet melalui wifi. Data di server dapat dilihat menggunakan website, selain itu juga data dapat dilihat pada aplikasi Telegram Messenger yang ter-install di perangkat ponsel. Program RPi2 dikembangkan menggunakan bahasa python dan komponen matplotlib. Hasil percobaan menunjukkan bahwa sistem memiliki prospek yang besar dan dapat digunakan untuk keperluan budidaya udang dengan memberikan informasi yang relevan dan tepat waktu. Data hasil pengumpulan tersebut dapat digunakan untuk penelitian dan analisa lebih lanjut.Â

Streaming Support for Data Intensive Cloud-Based Sequence Analysis

Cloud computing provides a promising solution to the genomics data deluge problem resulting from the advent of next-generation sequencing (NGS) technology. Based on the concepts of “resources-on-demand” and “pay-as-you-go”, scientists with no or limited infrastructure can have access to scalable and cost-effective computational resources. However, the large size of NGS data causes a significant data transfer latency from the client's site to the cloud, which presents a bottleneck for using cloud computing services. In this paper, we provide a streaming-based scheme to overcome this problem, where the NGS data is processed while being transferred to the cloud. Our scheme targets the wide class of NGS data analysis tasks, where the NGS sequences can be processed independently from one another. We also provide the elastream package that supports the use of this scheme with individual analysis programs or with workflow systems. Experiments presented in this paper show that our solution mitigates the effect of data transfer latency and saves both time and cost of computation