Changhyun Byun

Automated Twitter data collecting tool and case study with rule-based analysis

Applying data mining techniques to social media can yield interesting perspectives about individual human behavior, detecting hot issues and topics, or discovering a group and community. However, it is difficult to build your own data set to apply data mining techniques without an automated data gathering and filtering system because of main characteristics of social media: the data is large, noisy and dynamic. To overcome these challenges, we developed a java-based data gathering tool that continually collects social data from Twitter and filters noisy data. This allows us, as well as other researchers, to build our own Twitter database. In this paper, we introduce the design specifications and explain the implementation details of the Twitter Data Collecting Tool we developed. In addition, we provide an analysis of Twitter messages about various Super Bowl ads by applying data-mining techniques to a case study.

Automated Twitter data collecting tool for data mining in social network

Applying data mining techniques to social media can yield interesting perspectives about individual human behavior, detecting hot issues and topics, or discovering a group and community. However, it is difficult to build your own data set to apply data mining techniques without an automated data gathering system. To overcome this challenge, we developed a java-based data gathering tool that continually collects social data from Twitter. This allows us, as well as other researchers, to build our own Twitter database. In this paper, we introduce the design specifications and explain the implementation details of the Twitter Data Collecting Tool we developed. In addition, we provide an in-depth analysis of Twitter messages about various Super Bowl ads by applying data-mining techniques to a case study. The study aims to address the question of how people use Twitter and to assess the power of Twitter in terms of creating consumer interest in brands and commercials.

The XCREAM Framework and Collaboration Validity Tests

The purpose of the XCREAM (XLogic Collaborative RFID/USN-Enabled Adaptive Middleware) is to enable collaboration among many RFID/USN-enabled appplications by providing them with flexible interface to the XCREAM through a web-based service scheme, called the Enterprise Manager, and XML infrastructure language, the XLogic script language. Rapidly growing demand for collaboration among numerous and heterogeneous business applications draws attention to this kind of framework, especially in the ubiquitous computing environment. The XCREAM framework gathers massive events from a variety of event sources and distributes them to the appropriate service parties depending on the predefined business scenarios. The scenarios are written in the XLogic script language and registered to the XCREAM framework. The paper includes simulation result which shows excellence in performance and validity of collaboration suppport of the XCREAM. Reviewing the test results, XCREAM works well, especially in the collaborative environment. This approach makes it possible to integrate many heterogeneous services with various data sources and to present a collaborative service framework.

Context-Aware Inference (CAI) Model on Smart Computing Environment

With the proliferation of smart computing equipment, the range of providing highly intelligent services is widely expanding in an attempt to include the present condition of each individual data source. Furthermore, broadly adopted RFID (Radio Frequency Identification) technology has led to the widespread application of automatic identification in numerous pervasive computing services including logistics, emergency control, and medical services. Another remarkable phenomenon is the explosively growing number of smartphone users, which allows them to stay connected to the Internet. The environment created by these factors, increases the need to make a decision out of the current context which can be inferred from various sensors and mobile devices. In response to the growing demand for the need to make a decision in the current context, the new XOnt Agent has been integrated into our collaboration middleware framework, the XCREAM (XLogic Collaborative RFID/USN-Enabled Adaptive Middleware). All the collected information should be examined too see if they meet any condition that triggers correspondent actions of a specific rule through reasoning process of a rule engine. We also suggest the context-aware inference (CAI) model to effectively support the reasoning and handling processes.

Mobile Real-Time Tracking System Based on the XCREAM (XLogic Collaborative RFID/USN-Enabled Adaptive Middleware)

The main objective of this paper is to provide people who use smartphones with a convenient way of getting useful information by making their smartphones communicate with the XCREAM (XLogic Collaborative RFID/USN-Enabled Adaptive Middleware). We have developed the XCREAM, which is a smart mediator between many types of sensor devices and applications. It gathers a variety of raw data from many types of USN devices, such as temperature or humidity sensors and RFID readers. It also distributes the collected event data to the appropriate applications according to the pre-registered scenarios. In addition, we have been extending the external interface of the XCREAM, where the XCREAM receives data from smartphones as well as ordinary USN devices. Furthermore, the extension enables the XCREAM to deliver the sensor data to the corresponding smartphone(s) in order to make applications for the smartphone(s) be more flexible and interactive. In this paper, we present a simple but feasible scenario that helps a package receiver simply track the current location of its delivery truck for the package.

Dynamic Seed Analysis in a Social Network for Maximizing Efficiency of Data Collection

Applying data mining techniques to social media can yield interesting perspectives to understanding individual and human behavior, detecting hot issues and topics, or discovering a group and community. However, it is difficult to gather the data related to a specific topic due to the main characteristics of social media data sets: data is large, noisy, and dynamic. To collect the data related to a specific topic and keyword efficiently, we propose a new algorithm that selects the best seed nodes with limited resources and time. The algorithm also evaluates various user influence and activity factors, and updates the seed nodes dynamically during the gathering process. Furthermore, we compare two data sets collected by the algorithm and existing approaches.

Twitter data collecting tool with rule‐based filtering and analysis module

Purpose – It is difficult to build our own social data set because data in social media is generally too vast and noisy. The aim of this study is to specify design and implementation details of the Twitter data collecting tool with a rule‐based filtering module. Additionally, the paper aims to see how people communicate with each other through social networks in a case study with rule‐based analysis.Design/methodology/approach – The authors developed a java‐based data gathering tool with a rule‐based filtering module for collecting data from Twitter. This paper introduces the design specifications and explain the implementation details of the Twitter Data Collecting Tool with detailed Unified Modeling Language (UML) diagrams. The Model View Controller (MVC) framework is applied in this system to support various types of user interfaces.Findings – The Twitter Data Collecting Tool is able to gather a huge amount of data from Twitter and filter the data with modest rules for complex logic. This case study sh...

XCREAM: collaborative middleware framework for RFID/USN-enabled applications

The purpose of the XCREAM (XLogic Collaborative RFID/USN-Enabled Adaptive Middleware) is to enable collaboration among many RFID/USN-enabled applications by providing them with flexible interface to the XCREAM through a web-based service scheme, called the Enterprise Manager, and XML infrastructure language, the XLogic script language. The XCREAM framework gathers massive events from a variety of event sources and distributes them to the appropriate service parties depending on the predefined business scenarios. The scenarios are written in the XLogic script language and registered to the XCREAM framework. The paper includes simulation result which includes the performance and collaboration validity of the XCREAM. This approach makes it possible to integrate many heterogeneous services and to present a collaborative service framework.

Architecture of the remote routing validation tool for BGP anomaly detection

The Border Gateway Protocol (BGP) is an Inter-domain routing protocol that has gradually evolved over the past few decades. The main functionality of BGP is to exchange Network Layer Reachability Information (NLRI) between ASes so that a BGP speaker can find a better path to the destination of packets. However, BGP is highly vulnerable to a variety of attacks such as IP hijacking, masquerade, and Denial of Service (DOS). In order to protect BGP, there is a plethora of research on BGP security, such as Secure BGP (S-BGP), Secure Origin BGP (SO-BGP), Pretty Secure BGP (psBGP), Pretty Good BGP (pgBGP), and so on. In this paper, we propose Architecture of the Remote Routing Validation Tool (RRVT) which provides a communication channel between ordinary BGP routers and BGP-SRx server so that the ordinary BGP routers can validate BGP messages. BGP-SRx server, developed by NIST, offers the interface to the RPKI/ROA validation Cache using the RPKI.

Efficient Keyword-Related Data Collection in a Social Network with Weighted Seed Selection

Data mining in a social can yield interesting perspectives to understanding human behavior or detecting topics or communities. However, it is difficult to gather the data related to a specific topic due to the main characteristics of social media data: large, noisy, and dynamic. To collect the data related to a specific topic efficiently, we propose a new algorithm that selects better seeds with limited resources. Furthermore, we compare two data sets collected by the algorithm and existing approaches.