New healthcare record management (HRM) systems have been introduced as technology has evolved to provide more efficient care. Since medical data is usually sensitive and must be protected from unauthorized access, attention must be paid to data integrity, patient privacy, and storage. Blockchain technology has been proposed in the literature to integrate healthcare information systems through a decentralized and unified network. However, the literature on blockchain in healthcare is full of promises that may not be true under certain conditions. In our paper, we evaluate the veracity and sophistication of some of the claims made in the literature. We go beyond performing a literature review and shed light on the weak technical aspects claimed about blockchain. In addition, we benefit from our technical assessment and suggest some future research directions to improve healthcare systems that use blockchain and big data solutions.

Although blockchain was proposed in 2008 to solve the centralization problem in information exchange and eliminate the need for trusted third parties such as intermediaries or banks to conduct financial transactions, private blockchain applications today still suffer from the centralization problem. This situation has arisen because we cannot manage metadata on private or public blockchains, but a representation of this data as hashes or key values when the metadata is registered on central servers or the cloud. In this study, we highlight two decentralized storage platforms (IPFS and Sia) that can solve the centralization problem of current blockchain applications. We believe this work will be helpful for anyone working in Big Data and information systems related-fields.

The concept of smart cities has gained popularity due to technological advances in areas such as the Internet of Things (IoT) and Big Data Analytics (BDA). Location-based services have emerged in such smart environments to improve people’s quality of life and generate statistics for mutual benefit. In this work, a stylized presence detection concept is proposed which uses Bluetooth Low Energy (BLE) beacons placed in locations of interest. Users can detect the BLE beacon identification number (ID) with personal devices such as cell phones and connected watches and transmit it along with a unique and randomly generated user ID. Blockchain technology is used for a storage back-end. Our proposal is by no means exhaustive and is intended to advance the discussion of location-based services that deal with big data.

Many real-world complex systems can be modelled by temporal networks. Representation learning on these networks often captures their dynamic evolution and is a first step for performing further analysis, e.g. node classification. Node classification is a fundamental task for graph analysis in general and in the context of temporal graph, is often employed to categories nodes based on their activity patterns. Analysis of existing real world networks from different high-stake domains reveals that the rate of the malicious activities is on uptick, resulting in catastrophic social or economic consequences. This strongly motivates designing accurate node classification methods for temporal graphs. In this paper, we propose TGbase, for node classification on weighted temporal networks. TGbase efficiently extracts key features to consider the structural characteristics of each node and its neighborhood as well as the intensity and timestamp of the interactions among node pairs. These features accurately differentiate different classes of nodes, as shown on eight real-world benchmark datasets, outperforming multiple state-of-the-art (SOTA) deep/complex models. Our strong yet simple model is also generic, whereas the SOTA contenders are designed often for their specific (class of) datasets.

Blockchain technology is enabled by consensus algorithms to manage the relationships among several economic or business operators without human intervention. With the help of consensus algorithms, distributed systems can reliably reach agreement even if part of the system is faulty. Blockchain yields many benefits, among others, traceability, transparency, and security. We consider using the RAFT consensus algorithm to achieve robust and scalable decentralized applications, with focus on healthcare. We propose a stylized healthcare network, enabled by RAFT and built upon Hyperledger Fabric to showcase the use of RAFT in healthcare blockchain. However, RAFT is by no means limited to healthcare record systems, and can be applied to any other record system and value chain. Our paper offers several insights to those working in value chains and information management-related fields. In addition, we end our study with some future research avenues that may inspire managers and scholars to build or refine new decentralized systems in healthcare and other related fields.