Jason J. Jung

Social big data

Abstract Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media and big data paradigms.

Towards Semantic Social Networks

Computer manipulated social networks are usually built from the explicit assertion by users that they have some relation with other users or by the implicit evidence of such relations (e.g., co-authoring). However, since the goal of social network analysis is to help users to take advantage of these networks, it would be convenient to take more information into account. We introduce a three-layered model which involves the network between people (social network), the network between the ontologies they use (ontology network) and a network between concepts occurring in these ontologies. We explain how relationships in one network can be extracted from relationships in another one based on analysis techniques relying on this network specificity. For instance, similarity in the ontology network can be extracted from a similarity measure on the concept network. We illustrate the use of these tools for the emergence of consensus ontologies in the context of semantic peer-to-peer systems.

Service chain-based business alliance formation in service-oriented architecture

Businesses in service-oriented architecture (SOA) are usually flexible to be integrated with each other, so that they can discover, select, and compose services to fulfill a given task and goal. However, the number of businesses and services are ever-increasing, and more seriously, they are complexly linked with each other. These problems cause less flexibility of businesses, i.e., low understandability on business services and inefficiency on business integration. Thereby, in this paper, we propose a novel SOA-based platform to discover a service chain where (i) nodes are a set of services provided by businesses, and (ii) arcs indicate successive relationships between the services. We emphasize that we have to take into account not only explicit relationships (e.g., agreement) but also implicit ones by matching semantics of the services. Consequently, people like decision makers can easily figure out which businesses might be chosen in a certain situation. More importantly, business alliance formation process, which sorts out and merge relevant businesses, can be conducted by employing social network analysis (SNA) methods and finding out social relationships (e.g., centrality) between the corresponding services.

Semantic business process integration based on ontology alignment

Innovation and agility should be provided to businesses by efficient collaboration (i.e., communication and sharing) between them. However, semantic heterogeneity between business processes is a serious problem for automatically supporting cooperation processes (e.g., knowledge sharing and querying-based interactions) between businesses. In order to overcome this problem, we propose a novel framework based on aligning business ontologies for integrating heterogeneous business processes. We can consider two types of alignment processes; (i) manual alignment for building a whole business process ontology in a business process management (BPM) system and (ii) automated alignment between business processes of different BPM systems. Thereby, the optimal integration between two business processes has to be discovered to maximize the summation of a set of partial similarities between semantic components consisting of the business processes. In particular, the semantic component are extracted from semantic annotations of business processes. For evaluating the proposed system, we have conducted experimentations by using 22 business process management systems, which are organized as six business alliances. We have assumed that business processes in a same BPM system should be built with a common ontologies. The proposed alignment method has shown about 71.3% of precision (65.4% of recall). In addition, we found out that alignment results are dependent on some characteristics of ontologies (e.g., depth and number of classes).

Examination of how social aspects moderate the relationship between task characteristics and usage of social communication technologies (SCTs) in organizations

Because of the increasing significance of social communication technologies within an organization, they have become a new form of information processing, resulting in business process transitions and increased benefits. By applying media richness theory and social theories, this study investigated how social communication technologies (SCTs) can be used by an employee to fit his/her task characteristics. Additionally, it also examined how the employees social relationships moderated media usage in the current job environment and how this usage influenced the task performance. Five media were selected in this study (telephone, video conferencing, email, instant messaging, and blog). Using a hierarchical regression approach, we found that task characteristics were related to media usage, whereas social factors (social influence and social affinity) moderated the degree of the relationships. A few particular media and technologies seemed to perform well, however these are influenced by the social aspects. Moreover, the usage of social technologies results in positive task performance. The performance of a few specific technologies demonstrated binding effects (email performance was associated with instant messenger performance). In summary, we found that the usage of SCTs is instrumentally determined by the interaction between the task and social relationships.

Evolutionary approach for semantic-based query sampling in large-scale information sources

Metadata about information sources (e.g., databases and repositories) can be collected by Query Sampling (QS). Such metadata can include topics and statistics (e.g., term frequencies) about the information sources. This provides important evidence for determining which sources in the distributed information space should be selected for a given user query. The aim of this paper is to find out the semantic relationships between the information sources in order to distribute user queries to a large number of sources. Thereby, we propose an evolutionary approach for automatically conducting QS using multiple crawlers and obtaining the optimized semantic network from the sources. The aim of combining QS and evolutionary methods is to collaboratively extract metadata about target sources and optimally integrate the metadata, respectively. For evaluating the performance of contextualized QS on 122 information sources, we have compared the ranking lists recommended by the proposed method with user feedback (i.e., ideal ranks), and also computed the precision of the discovered subsumptions in terms of the semantic relationships between the target sources.

Contextualized mobile recommendation service based on interactive social network discovered from mobile users

Personal context is the most significant information for providing contextualized mobile recommendation services at a certain time and place. However, it is very difficult for service providers to be aware of the personal contexts, because each persons activities and preferences are very ambiguous and depending on numerous unknown factors. In order to deal with this problem, we have focused on discovering social relationships (e.g., family, friends, colleagues and so on) between people. We have assumed that the personal context of a certain person is interrelated with those of other people, and investigated how to employ his neighbors contexts, which possibly have a meaningful influence on his personal context. It indicates that we have to discover implicit social networks which express the contextual dependencies between people. Thereby, in this paper, we propose an interactive approach to build meaningful social networks by interacting with human experts. Given a certain social relation (e.g., isFatherOf), this proposed systems can evaluate a set of conditions (which are represented as propositional axioms) asserted from the human experts, and show them a social network resulted from data mining tools. More importantly, social network ontology has been exploited to consistently guide them by proving whether the conditions are logically verified, and to refine the discovered social networks. We expect these social network is applicable to generate context-based recommendation services. In this research project, we have applied the proposed system to discover the social networks between mobile users by collecting a dataset from about two millions of users.

Reusing ontology mappings for query routing in semantic peer-to-peer environment

To efficiently support automated interoperability between ontology-based information systems in distributed environments, the semantic heterogeneity problem has to be dealt with. To do so, traditional approaches have acquired and employed explicit mappings between the corresponding ontologies. Usually these mappings can be only obtained from human domain experts. However, it is too expensive and time-consuming to collect all possible mapping results on distributed information systems. More seriously, as the number of systems in a large-scale peer-to-peer (P2P) network increases, the efficiency of the ontology mapping is exponentially decreased. Thereby, in this paper, we propose a novel semantic P2P system, which is capable of (i) sharing and exchanging existing mappings among peers, and (ii) composing shared mappings to build a certain path between two systems. Given two arbitrary peers (i.e., source and destination), the proposed system can provide indirect ontology mappings to make them interoperable. In particular, we have focused on query-based communication for evaluating the proposed ontology mapping composition system. Once direct ontology mappings are collected from candidate peers, a given query can be (i) segmented into a set of sub-queries, and (ii) transformed to another query. With respect to the precision performance, our experimentation has shown an improvement of about 42.5% compared to the keyword-based query searching method.

Online named entity recognition method for microtexts in social networking services: A case study of twitter

Named entity recognition (NER) methods have been regarded as an efficient strategy to extract relevant entities for answering a given query. The aim of this work is to exploit the conventional NER methods for analyzing a large set of microtexts of which lengths are short. Particularly, the microtexts are streaming on online social media, e.g., Twitter. To do so, this paper proposes three properties of contextual association among the microtexts to discover contextual clusters of the microtexts, which can be expected to improve the performance of NER tasks. As a case study, we have applied the proposed NER system to Twitter. Experimental results demonstrate the feasibility of the proposed method (around 90.3% of precision) for extracting relevant information in online social network applications.

Taxonomy alignment for interoperability between heterogeneous virtual organizations

Resources in virtual organizations are classified based on their local taxonomies. However, heterogeneity between these taxonomies is a serious problem for efficient cooperation processes (e.g., knowledge sharing and querying-based interactions). In order to overcome this problem, we propose a novel framework based on aligning the taxonomies of virtual organizations. Thereby, the best mapping between two organization taxonomies has to be discovered to maximize the summation of a set of partial similarities between concepts in the taxonomies. We can consider two levels of alignment processes; (i) intra-alignment in a virtual organization for building an organizational taxonomy and (ii) inter-alignment between organizational taxonomies. Particularly, for intra-alignment, features extracted from resources are exploited to enhance the precision of similarity measurement between concepts. For experimentation, twelve virtual organizations have been built with different local taxonomies. The proposed inter-alignment method has shown about 76% of precision and 68% of recall. Also, feature-based intra-alignment improved those performance, during resource retrieval by query transformation. In addition, we found out that alignment results are dependent on some characteristics of taxonomies (e.g., depth and number of classes).