Fundamentals of a Novel Debugging Mechanism for Orchestrated Cloud Infrastructures with Macrosteps and Active Control

In Infrastructure-as-a-Service (IaaS) clouds, the development process of a ready-to-use and reliable infrastructure might be a complex task due to the interconnected and dependent services that are deployed (and operated later on) in a concurrent way on virtual machines. Different timing conditions may change the overall initialisation method, which can lead to abnormal behaviour or failure in the non-deterministic environment. The overall motivation of our research is to improve the reliability of cloud-based infrastructures with minimal user interactions and significantly accelerate the time-consuming debugging process. This paper focuses on the behaviour of cloud-based infrastructures during their deployment phase and introduces the adaption of a replay, and active control enriched debugging technique, called macrostep, in the field of cloud orchestration in order to provide support for developers troubleshooting deployment-related errors. The fundamental macrostep mechanisms, including the generation of collective breakpoint sets as well as the traversal method for such consistent global states, have been combined with the Occopus cloud orchestrator and the Neo4J graph database. The paper describes the novel approach, the design choices as well as the implementation of the experimental debugger tool with a use case for validation purposes by providing some preliminary numerical results.

Understanding Horizon 2020 Data: A Knowledge Graph-Based Approach

This paper aims to meaningfully analyse the Horizon 2020 data existing in the CORDIS repository of EU, and accordingly offer evidence and insights to aid organizations in the formulation of consortia that will prepare and submit winning research proposals to forthcoming calls. The analysis is performed on aggregated data concerning 32,090 funded projects, 34,295 organizations participated in them, and 87,067 public deliverables produced. The modelling of data is performed through a knowledge graph-based approach, aiming to semantically capture existing relationships and reveal hidden information. The main contribution of this work lies in the proper utilization and orchestration of keyphrase extraction and named entity recognition models, together with meaningful graph analytics on top of an efficient graph database. The proposed approach enables users to ask complex questions about the interconnection of various entities related to previously funded research projects. A set of representative queries demonstrating our data representation and analysis approach are given at the end of the paper.

Implementation of Classification Algorithms in Neo4j using IPL data

The main objective of this paper is to implement the classifications algorithms in Neo4j graph database using cypher query language. For implementing the classification algorithm, we have used Indian Premier League (IPL) dataset to predict the winner of the matches using some different features. The IPL is the most popular T20 cricket league in the world. The prediction models are based on the city where the matches were played, winner of the toss and decision of the toss. In this paper we have implemented Naïve Bayes and K-Nearest Neighbors (KNN) classification algorithms using cypher query language. Different classifiers are used to predict the outcome of different games like football, volleyball, cricket etc, using python and R. In this paper we shall use cypher query language. We shall also compare and analysis the results which are given by Naïve Bayes and K-Nearest Neighbors algorithms to predict the winner of the matches.

The Aliment to Bodily Condition knowledgebase (ABCkb): a database connecting plants and human health

Objective Overconsumption of processed foods has led to an increase in chronic diet-related diseases such obesity and type 2 diabetes. Although diets high in fresh fruits and vegetables are linked with healthier outcomes, the specific mechanisms for these relationships are poorly understood. Experiments examining plant phytochemical production and breeding programs, or separately on the health effects of nutritional supplements have yielded results that are sparse, siloed, and difficult to integrate between the domains of human health and agriculture. To connect plant products to health outcomes through their molecular mechanism an integrated computational resource is necessary. Results We created the Aliment to Bodily Condition Knowledgebase (ABCkb) to connect plants to human health by creating a stepwise path from plant $$\rightarrow$$ → plant product $$\rightarrow$$ → human gene $$\rightarrow$$ → pathways $$\rightarrow$$ → indication. ABCkb integrates 11 curated sources as well as relationships mined from Medline abstracts by loading into a graph database which is deployed via a Docker container. This new resource, provided in a queryable container with a user-friendly interface connects plant products with human health outcomes for generating nutritive hypotheses. All scripts used are available on github (https://github.com/atrautm1/ABCkb) along with basic directions for building the knowledgebase and a browsable interface is available (https://abckb.charlotte.edu).

Sistem Rekomendasi Pembelian Produk Kesehatan pada E-Commerce ABC berbasis Graph Database Amazon Neptune menggunakan Metode Hybrid Content-Collaborative Filtering

Recommendation System of Health Product Purchasing at ABC E-Commerce System based on Amazon Neptune’s Graph Database using Hybrid ContentCollaborative Filtering Method.Health products purchased by society, either in drugstores or pharmacies may vary according to their needs. ABC e-commerce is a Business to Business (B2B)-based e-commerce owned by PT XYZ. As a health product sales system from distributors to drug stores/pharmacies, they still do not have a health product purchase recommendation system yet. The recommendation system is needed to provide recommendations of health products for the customers. Amazon Neptune is implemented in this research to build a health product recommendation system. The hybrid contentcollaborative filtering method is used to generate complete recommendations based on content attributes and user habits. The datasets were product data, product categories, customers, product principals, and data of products trading. This research produces a health products recommendations model at ABC e-commerce with android based using web services. The implementation can provide recommendations of health products that can be accessed in real-time by customers.Keywords: health products, recommendation systems, graph database, Amazon Neptune, hybrid content-collaborative filteringAbstrak. Produk kesehatan yang dibeli masyarakat, melalui toko obat/apotek, dapat berbeda sesuai kebutuhan. E-commerce ABC berbasis Business to Business (B2B) milik PT XYZ sebagai sistem penjualan produk kesehatan dari distributor kepada toko obat/apotek belum memiliki sistem rekomendasi pembelian produk kesehatan. Sistem rekomendasi sebagai pengembangan fitur e-commerce ABC diperlukan untuk memberikan rekomendasi produk kesehatan yang sesuai dengan keadaan setiap pelanggan. Amazon Neptune sebagai graph database service yang dapat mengelola relasi dalam data yang saling terhubung, digunakan dalam penelitian untuk membangun sistem rekomendasi produk kesehatan. Metode hybrid content-collaborative filtering digunakan untuk menghasilkan rekomendasi yang lengkap berdasarkan atribut konten dan kebiasaan pengguna. Dataset yang digunakan meliputi data produk, kategori produk, pelanggan, principal, serta data jual-beli produk di e-commerce ABC. Penelitian ini menghasilkan model rekomendasi produk kesehatan yang diimplementasikan pada e-commerce ABC berbasis Android menggunakan web service. Implementasi tersebut memberikan rekomendasi produk kesehatan yang dapat diakses secara real-time oleh pelanggan pada saat menggunakan ecommerce ABC.Kata Kunci: produk kesehatan, sistem rekomendasi, graph database, Amazon Neptune, hybrid content-collaborative filtering