SUMMARY The growing number of services processed and stored in the cloud has led to difficulties in managing and discovering the required services efficiently. Multilevel index model is an efficient method to manage and retrieve services in service repositories. When adding a new service to a multilevel index model, a key needs to be selected for the service, but existing key selection methods cannot adapt to the situation that hot services change over time. To address this problem, this article proposes an adaptive key selection method to improve the efficiency of service retrieval. However, the service addition operation of the adaptive key selection method is inefficient in the multilevel index model. For this reason, this article improves the multilevel index model by introducing local equivalence partition. This indexing model improves the service addition efficiency of the adaptive key selection method without affecting the service retrieval efficiency. It is experimentally demonstrated that the retrieval and addition efficiencies of the adaptive key selection method are close to the ideal state optimum under the multilevel index model with local equivalence partitioning.

Bitcoin transaction analysis is valuable for examining Bitcoin events. However, most of the existing methods are inadequate for dealing with transactions involving multiple entities. Furthermore, existing Bitcoin transaction analysis methods neglect to evaluate the influence of different entities on a Bitcoin event. This article aims to overcome such limitations by introducing a novel method for multi-entity Bitcoin transaction analysis along with proposing a method for multi-entity influence assessment based on the Bitcoin transaction network (BTN) model. To overcome the loss of tracking information, a Bitcoin gene operation named compound dyeing is devised and incorporated into the BTN simulation. After obtaining the simulation results, a method for multi-entity transaction behavior analysis is presented to identify and visualize the interactions among entities precisely and effectively. Furthermore, four influence indices with suitable visualization methods are proposed based on the features of the BTN to measure the business and trading influences of different entities. A real-world case study, the Mt.Gox coin loss event, is analyzed to demonstrate the effectiveness and efficiency of the proposed methods.

Abstract With the development of edge computing and artificial intelligence (AI) technologies, edge devices are witnessed to generate data at unprecedented volume. The edge intelligence (EI) has led to the emergence of edge devices in various application domains. The EI can provide efficient services to delay‐sensitive applications, where the edge devices are deployed as edge nodes to host the majority of execution, which can effectively manage services and improve service discovery efficiency. The multilevel index model is a well‐known model used for indexing service, such a model is being introduced and optimized in the edge environments to efficiently services discovery while managing large volumes of data. However, effectively updating the multilevel index model by adding new services timely and precisely in the dynamic edge computing environments is still a challenge. Addressing this issue, this article proposes a designated key selection method to improve the efficiency of adding services in the multilevel index models. Our experimental results show that in the partial index and the full index of multilevel index model, our method reduces the service addition time by around 84% and 76%, respectively when compared with the original key selection method and by around 78% and 66%, respectively when compared with the random selection method. Our proposed method significantly improves the service addition efficiency in the multilevel index model, when compared with existing state‐of‐the‐art key selection methods, without compromising the service retrieval stability to any notable level.

Online social networks (OSNs) have emerged as a major platform for sharing information through social relationships and are one of the major sources of big data. Social networks can even accommodate sharing of live streaming data among the connected users. However, social information on social networks is often locally exploited rather than capturing the changes in the entire network over time. Obtaining user's influence statistics is limited only in their local vicinity, which may not facilitate capturing the changes in the user and post influences across the entire network, thereby resulting in lower accuracy while measuring user's topical influence. Moreover, low-influence users always exist in the network publishing low-quality posts. With the objectives of accurately capturing highly influential users and posts, this article proposes a novel dynamic social sensing model, named dynamic PageRank (DPRank) model, to evaluate the dynamic topical influence of the users of social information on social networks during the social information evolution. We deploy our proposed model to real-world Twitter data sets, which demonstrates the effectiveness of our proposed model against notable existing methods while identifying the true influence of users and posts in a dynamically evolving social network.

Bitcoin [1] as a popular digital currency has been a target of theft and other illegal activities. Key to the forensic investigation is to identify bitcoin addresses involved in bitcoin transfers. This paper presents a framework, FABT, for forensic analysis of bitcoin transactions by identifying suspicious bitcoin addresses. It formalizes the clues of a given case as transaction patterns defined over a comprehensive set of features. FABT converts the bitcoin transaction data into a formal model, called Bitcoin Transaction Net (BTN). The traverse of all bitcoin transactions in the order of their occurrences is captured by the firing sequence of all transitions in the BTN. We have applied FABT to identify suspicious addresses in the Mt.Gox case. A subgroup of the suspicious addresses has been found to share many characteristics about the received/transferred amount, number of transactions, and time intervals.

Summary There are many different index structures for service repositories, such as sequential index, inverted index, and multilevel indices that include three deployments. Different service sets maybe have different characteristics that may affect performance from different aspects. For a given service set, which index structure is the most optimal one? To address these issues, this paper analyses five indexing models and proposes expectation of traversed service count to estimate performance of service retrieval. Based on these expectation formulas, an optimal deployment method can be identified to maximize efficiency of service retrieval. Our experiments first validate correctness of the proposed formulas and then validate the effective of the optimal method.

Microblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation.

Given the recent proliferation in the number of smart devices connected to the Internet, the era of Internet of Things (IoT) is challenged with massive amounts of data generation. Fog Computing is gaining popularity and is being increasingly deployed in various latency-sensitive application domains including industrial IoT. However, efficient discovery of services is one of the prevailing issues in the fog nodes of industrial IoT, which restrain their efficiencies in availing appropriate services to the clients. To address this issue, this paper proposes a novel efficient multilevel index model based on equivalence relation, named the distributed multilevel (DM)-index model, for service maintenance and retrieval in the fog layer of industrial IoT to eliminate redundancy, narrow the search space, reduce both the number of traversed services and retrieval time, ultimately to improve the service discovery efficiency. The efficiency of the proposed index model has been verified theoretically and evaluated experimentally, which demonstrates that the proposed model is effective in achieving much better service discovery and retrieval performance than the sequential and inverted index models.

With the increasing popularity and rapid development of Online Social Networks (OSNs), OSNs not only bring fundamental changes to information and communication technologies, but also make an extensive and profound impact on all aspects of our social life. Efficient content discovery is a fundamental challenge for large-scale distributed OSNs. However, the similarity between social networks and online social networks leads us to believe that the existing social theories are useful for improving the performance of social content discovery in online social networks. In this article, we propose an interest-aware social-like peer-to-peer (IASLP) model for social content discovery in OSNs by mimicking ten different social theories and strategies. In the IASLP network, network nodes with similar interests can meet, help each other, and co-operate autonomously to identify useful contents. The presented model has been evaluated and simulated in a dynamic environment with an evolving network. The experimental results show that the recall of IASLP is 20% higher than the existing method SESD while the overhead is 10% lower. The IASLP can generate higher flexibility and adaptability and achieve better performance than the existing methods.

The number of web services has grown drastically. Then how to manage them efficiently in a service repository is an important issue to address. Given a special field, there often exists an efficient data structure for a class of objects, e.g., the Google' Bigtable is very suitable for webpages' storage and management. Based on the theory of the equivalence relations and quotient sets, this work proposes a multilevel index model for large-scale service repositories, which can be used to reduce the execution time of service discovery and composition. Its novel use of keys as inspired by the key in relational database can effectively remove the redundancy of the commonly-used inverted index. Its four function-based operations are for the first time proposed to manage and maintain services in a repository. The experiments validate that the proposed model is more efficient than the existing structures, i.e., sequential and inverted index ones.

With the globe facing various scales of natural disasters then and there, disaster recovery is one among the hottest research areas and the rescue and recovery services can be highly benefitted with the advancements of information and communications technology (ICT). Enhanced rescue effect can be achieved through the dynamic networking of people, systems and procedures. A seamless integration of these elements along with the service-oriented systems can satisfy the mission objectives with the maximum effect. In disaster management systems, services from multiple sources are usually integrated and composed into a usable format in order to effectively drive the decision-making process. Therefore, a novel service indexing method is required to effectively discover desirable services from the large-scale disaster service repositories, comprising a huge number of services. With this in mind, this paper presents a novel multilevel indexing algorithm based on the equivalence theory in order to achieve effective service discovery in large-scale disaster service repositories. The performance and efficiency of the proposed model have been evaluated by both theoretical analysis and practical experiments. The experimental results proved that the proposed algorithm is more efficient for service discovery and composition than existing inverted index methods.

Owing to their rapid development, services are increasing rapidly in quantity. The consequence is that there are so many services that share the same or similar functions. Therefore, it is important to select a credible and optimal service. Reputation as one of the important parameters of services plays a significant role in the decision support for service selection. This paper proposes a novel two-phase method to calculate service reputation. The first phase uses a dynamic weight formula to calculate reputation such that it can reflect the latest tendency of a service. The second one uses an olfactory response formula to mitigate the negative effect of unfair ratings. Some experiments are conducted and the results validate the effectiveness of the proposed method.

With the rapid development of service-oriented computing (SOC) and service-oriented architecture (SOA), the number of services is rapidly increasing. How to organize and manage services effectively in repositories to improve the efficiency of service discovery and composition is important. This paper proposes three categorization rules to classify services for a large scale repository to form a relational taxonomy. The service retrieve scope can be drastically narrowed by this taxonomy. Therefore, the efficiency of service discovery and service composition can be greatly improved. We evaluate and compare the performance of the proposed method and other related ones via a publicly available test set, ICEBE05. The experimental results validate the effectiveness and high efficiency of the proposed one.