Background and Objective Processing of medical imaging big data is deeply challenging due to the size of data, computational complexity, security storage and inherent privacy issues. Traditional picture archiving and communication system, which is an imaging technology used in the healthcare industry, generally uses centralized high performance disk storage arrays in the practical solutions. The existing storage solutions are not suitable for the diverse range of medical imaging big data that needs to be stored reliably and accessed in a timely manner. The economical solution is emerging as the cloud computing which provides scalability, elasticity, performance and better managing cost. Cloud based storage architecture for medical imaging big data has attracted more and more attention in industry and academia. Methods This study presents a novel, fast and scalable framework of medical image storage service based on distributed file system. Two innovations of the framework are introduced in this paper. An integrated medical imaging content indexing file model for large-scale image sequence is designed to adapt to the high performance storage efficiency on distributed file system. A virtual file pooling technology is proposed, which uses the memory-mapped file method to achieve an efficient data reading process and provides the data swapping strategy in the pool. Result The experiments show that the framework not only has comparable performance of reading and writing files which meets requirements in real-time application domain, but also bings greater convenience for clinical system developers by multiple client accessing types. The framework supports different user client types through the unified micro-service interfaces which basically meet the needs of clinical system development especially for online applications. The experimental results demonstrate the framework can meet the needs of real-time data access as well as traditional picture archiving and communication system. Conclusions This framework aims to allow rapid data accessing for massive medical images, which can be demonstrated by the online web client for MISS-D framework implemented in this paper for real-time data interaction. The framework also provides a substantial subset of features to existing open-source and commercial alternatives, which has a wide range of potential applications.
Keywords: Hadoop distributed file system | Data packing | Memory mapping file | Message queue | Micro-service | Medical imaging

Physical world integration with cyber world opens the opportunity of creating smart environments; this new paradigm is called the Internet of Things (IoT). Communication between humans and objects has been extended into those between objects and objects. Industrial IoT (IIoT) takes benefits of IoT communications in business applications focusing in interoperability between machines (i.e., IIoT is a subset from the IoT). Number of daily life things and objects connected to the Internet has been in increasing fashion, which makes the IoT be the dynamic network of networks. Challenges such as heterogeneity, dynamicity, velocity, and volume of data, make IoT services produce inconsistent, inaccurate, incomplete, and incorrect results, which are critical for many applications especially in IIoT (e.g., health-care, smart transportation, wearable, finance, industry, etc.). Discovering, searching, and sharing data and resources reveal 40% of IoT benefits to cover almost industrial applications. Enabling real-time data analysis, knowledge extraction, and search techniques based on Information Communication Technologies (ICT), such as data fusion, machine learning, big data, cloud computing, blockchain, etc., can reduce and control IoT and leverage its value. This research presents a comprehensive review to study state-of-the-art challenges and recommended technologies for enabling data analysis and search in the future IoT presenting a framework for ICT integration in IoT layers. This paper surveys current IoT search engines (IoTSEs) and presents two case studies to reflect promising enhancements on intelligence and smartness of IoT applications due to ICT integration.
Keywords: Industrial IoT (IIoT) | Searching and indexing | Blockchain | Big data | Data fusion Machine learning | Cloud and fog computing

Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to realtime big data analytics and cybersecurity.
Keywords: Smart manufacturing | Big data | Cloud computing | Cloud manufacturing | Internet of things | NoSQL

Cyber–physical cloud systems (CPCS) are extensions of cyber–physical systems (CPS) that expand the cyber-part and distribute it on-device and in-cloud. CPCS are considered large-scale heterogeneous distributed cloud computing systems that support execution of multiple workflows. This study aims to reduce the energy consumption of multiple real-time workflows on CPCS and it contains two objectives: (1) maximizing the number of workflows that are completed within their deadlines; (2) minimizing the energy consumption of the workflows that are completed within their deadlines. The former is solved by proposing a deadline-driven processor merging for multiple workflows (DPMMW) algorithm, whereas the latter is solved by proposing a global energy saving for multiple workflows (GESMW) algorithm to minimize the total energy consumption. Experimental results validate that the combined DPMMW&GESMW algorithm can reduce deadline miss ratio (DMR) and save as much as possible energy over existing methods.
Keywords: Cyber–physical cloud systems (CPCS) | Deadline miss ratio (DMR) | Global energy saving (GES) | Multiple workflows | Real-time constraint

An exponential increase in the data generation resources is widely observed in last decade, because of evolution in technologies such as-cloud computing, IoT, social networking, etc. This enormous and unlimited growth of data has led to a paradigm shift in storage and retrieval patterns from traditional data structures to Probabilistic Data Structures (PDS). PDS are a group of data structures that are extremely useful for Big data and streaming applications in order to avoid high-latency analytical processes. These data structures use hash functions to compactly represent a set of items in stream-based computing while providing approximations with error bounds so that well-formed approximations get built into data collections directly. Compared to traditional data structures, PDS use much less memory and constant time in processing complex queries. This paper provides a detailed discussion of various issues which are normally encountered in massive data sets such as-storage, retrieval, query,etc. Further, role of PDS in solving these issues is also discussed where these data structures are used as temporary accumulators in query processing. Several variants of existing PDS along with their application areas have also been explored which give a holistic view of domains where these data structures can be applied for efficient storage and retrieval of massive data sets. Mathematical proofs of various parameters considered in the PDS have also been discussed in the paper. Moreover, the relative comparison of various PDS with respect to various parameters is also explored.
Keywords: Big data | Internet of things (IoT) | Probabilistic data structures | Bloom filter | Quotient filter | Count min sketch | HyperLogLog counter | Min-hash Locality | sensitive hashing