Human-Computer Interaction With Big Data Analytics

Big Data has been playing a vital role in almost all environments such as healthcare, education, business organizations and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Big data consists of huge unstructured data that require advance real-time analysis. Thus, nowadays many of the researchers are interested in developing advance technologies and algorithms to solve the issues when dealing with big data. Big Data has gained much attention from many private organizations, public sector and research institutes. This chapter provides an overview of the state-of-the-art algorithms for processing big data, as well as the characteristics, applications, opportunities and challenges of big data systems. This chapter also presents the challenges and issues in human computer interaction with big data analytics.

Big Data, Data Mining, and Data Analytics in IoT-Based Healthcare Applications

IoT seriously impacts every industry. The healthcare industry has experienced progression in digitizing medical records. Healthcare services are costlier than ever. Data mining is one of the largest challenges to face IoT. Big Data is an accumulation of data. IoT devices receive lots of data. Big data systems can do a lot of data analytics. The tools can also be used to perform these operations. The big health application system can be built by integrating medical health resources using intelligent terminals, internet of things (IoT), big data, and cloud computing. People suffer from many diseases. A big health system can be applied to scientific health management by detecting risk factors for the occurrence of diseases. Patients can have special attention to their health requirements and their devices can be tuned to remind them of their appointments, calorie count, exercise check, blood pressure variations, symptoms of any diseases, and so much more.

Big Data Analytics in Supply Chain Management

The current approach to supply chain intelligence has some fundamental challenges when confronted with the scale and characteristics of big data. In this chapter, applications, challenges and new trends in supply chain big data analytics are discussed and background research of big data initiatives related to supply chain management is provided. The methodology and the unified model for supply chain big data analytics which comprises the whole business intelligence (data science) lifecycle is described. It enables creation of the next-generation cloud-based big data systems that can create strategic value and improve performance of supply chains. Finally, example of supply chain big data solution that illustrates applicability and effectiveness of the model is presented.

Exploring parallel formal verification of BIG-DATA systems

Software Engineering is trying to adapt its tools, mechanisms and techniques to cope with the challenges involved when developing BIG DATA software systems. In particular, formal verification in one of the areas that more urgently is required to step in. In this work we introduce two crucial aspects to consolidate the FVS tool to tackle this issue. For one side, FVS’s parallel algorithm is proved to be sound and correct. For the other side, we developed a compelling empirical validation of our approach, employing a communication protocol relevant in the industrial world within a context of parallel systems, introducing a load-balancer process and comparing several implementations.

Ethical problems in the use of algorithms in data management and in a free market economy

The problem that I present in this paper concerns the issue of ethical evaluation of algorithms, especially those used in social media and which create profiles of users of these media and new technologies that have recently emerged and are intended to change the functioning of technologies used in data management. Systems such as Overton, SambaNova or Snorkel were created to help engineers create data management models, but they are based on different assumptions than the previous approach in machine learning and deep learning. There is a need to analyze both deep learning algorithms and new technologies in database management in terms of their actions towards a person who leaves their digital footprints, on which these technologies work. Then, the possibilities of applying the existing deep learning technology and new Big Data systems in the economy will be shown. The opportunities offered by the systems mentioned above seem to be promising for many companies and—if implemented on a larger scale—they will affect the functioning of the free market.

Improving Lookup and Query Execution Performance in Distributed Big Data Systems using Cuckoo Filter

Performance is a critical concern when reading and writing data from billions of records stored in Big Data warehouse. We introduce two scopes for query performance improvement. One is to improve performance of lookup queries after data deletion in Big Data systems that use Eventual Consistency. We propose a scheme to improve lookup performance after data deletion by using Cuckoo Filter. Another scope for improvement is to avoid unnecessary network round-trip for querying in remote nodes in a distributed Big Data cluster when it is known that the nodes do not have requested partition of data. We propose a scheme using probabilistic filters that are looked up before querying remote nodes, so that queries resulting in no data can be skipped from passing through the network. We evaluate our schemes with Cassandra using real dataset and show that each scheme can improve performance of lookup queries for up to 100%.