Jai E. Jung

Real-time event detection for online behavioral analysis of big social data

Abstract Social networking services are becoming increasingly popular during the daily lives of Internet citizens, especially since the advent of smart mobile devices with integrated utility modules such as 4G/WIFI connectivity, global positioning services, cameras, and heart beat sensors. Many devices are available for sharing information at any time, which can be listed by posting a photo, sharing a status, or narrating an event. The behavior of users means that the flow of data (or a social data stream) has real-time characteristics, which actually comprise notifications about your friends’ posts after a short delay for diffusion over the network. The data stream contains news pieces related to real social facts as well as unfocused information. In addition, important information (or events) attracts more public attention, which is demonstrated by the number of relevant messages or communication interactions between people interested in specific topics. From a technical perspective, the characteristics of data in the aforementioned scenario provide us with an opportunity to construct a model that can automatically determine the occurrence of events based on a social data stream. In this study, we propose an approach to solve the problem of early event identification, which requires appropriate approaches for processing incoming data in terms of the processing performance and number of data.

Exploiting character networks for movie summarization

Movie summarization focuses on providing as much information as possible for shorter movie clips while still keeping the content of the original movie and presenting a faster way for the audience to understand the movie. In this paper, we propose a novel method to summarize a movie based on character network analysis and the appearance of protagonist and main characters in the movie. Experiments were carried out for 2 movies (Titanic (1997) and Frozen (2013)) to show that our method outperforms conventional approaches in terms of the movie summarization rate.

Sequence Clustering-based Automated Rule Generation for Adaptive Complex Event Processing

In Complex Event Processing (CEP), complex events are detected according to a set of rules that are defined by domain experts. However, it makes the reliability of the system decreased as dynamic changes occur in the domain environment or domain experts make mistakes. To address such problem, this study proposes a Sequence Clustering-based Automated Rule Generation (SCARG) that can automatically generate rules by mining decision-making history of domain experts based on sequence clustering and probabilistic graphical modeling. Furthermore, based on a two-way learning approach, the proposed method is able to support automated regular or occasional rule updates. It makes self-adaptive CEP system possible by combining the rule generation method and the existing dynamic CEP systems. This technique is verified by establishing an automated stock trading system, and the performance of the system is measured in terms of the rate of return. The study solves the aforementioned problems and shows excellent results with an increase of 19.32% in performance when compared to the existing dynamic CEP technique. The paper presents a novel framework for complex event processing.The proposed method has been designed by temporal probabilistic model.It has been applied to stock trading system.

Weighted Similarity Schemes for High Scalability in User-Based Collaborative Filtering

Similarity-based algorithms, often referred to as memory-based collaborative filtering techniques, are one of the most successful methods in recommendation systems. When explicit ratings are available, similarity is usually defined using similarity functions, such as the Pearson correlation coefficient, cosine similarity or mean square difference. These metrics assume similarity is a symmetric criterion. Therefore, two users have equal impact on each other in recommending new items. In this paper, we introduce new weighting schemes that allow us to consider new features in finding similarities between users. These weighting schemes, first, transform symmetric similarity to asymmetric similarity by considering the number of ratings given by users on non-common items. Second, they take into account the habit effects of users are regarded on rating items by measuring the proximity of the number of repetitions for each rate on common rated items. Experiments on two datasets were implemented and compared to other similarity measures. The results show that adding weighted schemes to traditional similarity measures significantly improve the results obtained from traditional similarity measures.

Statistical approach for figurative sentiment analysis on Social Networking Services: a case study on Twitter

This paper presents a system that analyzes the sentiment of figurative language contained in short texts collected from Social Networking Services (SNS). This case study sources information from tweets on Twitter and calculates the polarity of the figurative language with three different categories (i.e., sarcastic, ironic, and metaphorical tweets). As in Medhat et al. (Ain Shams Eng J 5(4):1093–1113, 2014), Nguyen and Jung (Mob Netw Appl 20(4):475–486, 2015), many related works have used a lexical-based approach (e.g., dictionary and corpus), and machine learning-based approach (e.g., decision tree, rule discovery, and probabilistic methods) to extract sentiment in a given text. This statistical approach makes use of two main features: i) Content-based, and ii) Emotion Pattern-based. We believe that this combination offers a general method to solve the current problem and easily extends for analyzing other types of figurative languages. The proposed algorithm is evaluated by using Cosine similarity to conduct an experiment over a Data set that contains about 5,000 tweets. The results show that the FIS Model (Figurative language Identification using Statistical-based Model) works well with figurative tweets with a highest achievement of 0.7813.

Privacy-Preserving Discovery of Topic-Based Events from Social Sensor Signals: An Experimental Study on Twitter

Social network services (e.g., Twitter and Facebook) can be regarded as social sensors which can capture a number of events in the society. Particularly, in terms of time and space, various smart devices have improved the accessibility to the social network services. In this paper, we present a social software platform to detect a number of meaningful events from information diffusion patterns on such social network services. The most important feature is to process the social sensor signal for understanding social events and to support users to share relevant information along the social links. The platform has been applied to fetch and cluster tweets from Twitter into relevant categories to reveal hot topics.

A computational model of transmedia ecosystem for story-based contents

Story-based contents (e.g., novel, movies, and computer games) have been dynamically transformed into various media. In this environment, the contents are not complete in themselves, but closely connected with each other. Also, they are not simply transformed form a medium to other media, but expanding their stories. It is called as a transmedia storytelling, and a group of contents following it is called as a transmedia ecosystem. Since the contents are highly connected in terms of the story in the transmedia ecosystem, the existing content analysis methods are hard to extract relationships between the contents. Therefore, a proper content analysis method is needed with considering expansions of the story. The aim of this work is to understand how (and why) such contents are transformed by i) defining the main features of the transmedia storytelling and ii) building the taxonomy among the transmedia patterns. More importantly, computational transmedia ecosystem is designed to process a large number of the contents, and to support high understandability of the complex transmedia patterns.

Real-Time Traffic Flow Management Based on Inter-Object Communication: a Case Study at Intersection

Intersections become very congested when traffic volumes are high, creating inefficiency that results in user delay and frustration. There have been many approaches which focus on optimization signal of Traffic Light System and Vehicle Trajectory Analysis to improve traffic flow at intersection. However, to implement those approaches into reality become a challenges since real-time problem. In this study, inspired by recent advanced vehicle technologies, we propose an approach for traffic flow management at intersection. In particular, with the exploding at an enormous rate of Internet of Things (IoT), the connected object has been the most visible and familiar application. By this way, based on connected object, we design a model which communicating among objects to improve traffic flow at intersection with real time problem. Moreover, traffic congestion is also taken into consideration in case of high traffic volume. The simulation shows the potential results comparing with the existing traffic management system.

Event-Driven Trust Refreshment on Ambient Services

Since trust among entities can change according to various conditions, it is necessary for ambient services to determine when and how the trust has to be updated. Therefore, our contribution in this paper is to present: 1) a new definition of trust that can be extended to various domains; 2) a novel method based on social events and patterns to trigger trust refreshment in ambient services; and 3) a web application framework (called SocioScope) for collecting and analyzing data from multiple data sources. Finally, the case study suggests that this proposal could be applied to trust-aware ambient and recommendation systems.

Serendipity-based storification: from lifelogging to storytelling

Storification is a theoretical technique which aims to construct the underlying relationships from discrete information for packaging them into a logical structure. In this paper, we focus on proposing the definition of serendipity-based storification in the personal history which is the combination of two-step processes: i) discovering hidden stories in the personal history and ii) representing stories using visualization techniques for easily grasping the information. MyMovieHistory Hong & Jung (Cybern Syst 46 (1-2), 69–83 ??) is used as the case study to demonstrate the effect of storification through detecting and presenting patterns in real personal data. The results can be utilized in helping people easily memorize and comprehend their histories. Moreover, additional benefits (e.g., reminding the pass, predicting the future, and communicating who you are) can be gained through the use of storification in the personal history.