Byoungjip Kim

HiCon: a hierarchical context monitoring and composition framework for next-generation context-aware services

This article presents a hierarchical context monitoring and composition framework that effectively supports next-generation context-aware services. The upcoming ubiquitous space will be covered with innumerable sensors and tiny devices, which ceaselessly pump out a huge volume of data. This data gives us an opportunity for numerous proactive and intelligent services. The services require extensive understanding of rich and comprehensive contexts in real time. The framework provides three hierarchical abstractions: PocketMon (personal), HiperMon (regional), and EGI (global). The framework provides effective approaches to combining context from each level, thereby allowing us to create a rich set of applications, not possible otherwise. It deals with an extensively broad spectrum of contexts, from personal to worldwide in terms of scale, and from crude to highly processed in terms of complexity. It also facilitates efficient context monitoring and addresses the performance issues, achieving a high level of scalability. We have prototyped the proposed framework and several applications running on top of it in order to demonstrate its effectiveness.

BMQ-Index: Shared and Incremental Processing of Border Monitoring Queries over Data Streams

Border Monitoring Query (BMQ) has different query semantic from conventional continuous range query. It monitors the values of data streams and reports them only when data streams cross the borders of its range. In this paper, we first emphasize the importance and usefulness of BMQ through attractive service scenarios. Then, we propose BMQ-Index, which is specialized to BMQ evaluation. It efficiently processes a large number of BMQs in a shared and incremental manner. For shared processing, BMQ-Index adopts a query indexing approach, thereby achieving a high level of scalability. For incremental processing, BMQ-Index employs an incremental access method. Thus, successive BMQ evaluations are significantly accelerated. We present an index structure and a search algorithm to support onedimensional as well as multi-dimensional BMQ. Lastly, we demonstrate the performance benefits of BMQ-Index through analysis and experiments.

Scalable Activity-Travel Pattern Monitoring Framework for Large-Scale City Environment

In this paper, we introduce Activity Travel Pattern (ATP) monitoring in a large-scale city environment. ATP represents where city residents and vehicles stay and how they travel around in a complex megacity. Monitoring ATP will incubate new types of value-added services such as predictive mobile advertisement, demand forecasting for urban stores, and adaptive transportation scheduling. To enable ATP monitoring, we develop ActraMon, a high-performanceATP monitoring framework. As a first step, ActraMon provides a simple but effective computational model of ATP and a declarative query language facilitating effective specification of various ATP monitoring queries. More important, ActraMon employs the shared staging architecture and highly efficient processing techniques, which address the scalability challenges caused by massive location updates, a number of ATP monitoring queries and processing complexity of ATP monitoring. Finally, we demonstrate the extensive performance study of ActraMon using realistic city-wide ATP workloads.

Mobiiscape: Middleware support for scalable mobility pattern monitoring of moving objects in a large-scale city

With the explosive proliferation of mobile devices such as smartphones, tablets, and sensor nodes, location-based services are getting even more attention than before, considered as one of the killer applications in the upcoming mobile computing era. Developing location-based services necessarily requires an effective and scalable location data processing technology. In this paper, we present Mobiiscape, a novel location monitoring system that collectively monitors mobility patterns of a large number of moving objects in a large-scale city to support city-wide mobility-aware applications. Mobiiscape provides an SQL-like query language named Moving Object Monitoring Query Language (MQL) that allows applications to intuitively specify Mobility Pattern Monitoring Queries (MPQs). Further, Mobiiscape provides a set of scalable location monitoring techniques to efficiently process a large number of MPQs over a large number of location streams. The scalable processing techniques include a (1) Place Border Index, a spatial index for quickly searching for relevant queries upon receiving location streams, (2) Place-Based Window, a spatial-purpose window for efficiently detecting primitive mobility patterns, (3) Shared NFA, a shared query processing technique for efficiently matching complex mobility patterns, and (4) Attribute Pre-matching Bitmap, an in-memory data structure for efficiently filtering out moving objects based on their attributes. We have implemented a Mobiiscape prototype system. Then, we show the usefulness of the system by implementing promising location-based applications based on it such as a ubiquitous taxicab service and a location-based advertising. Also, we demonstrate the performance benefit of the system through extensive evaluation and comparison.

High-performance composite event monitoring system supporting large numbers of queries and sources

This paper presents a novel data structure, called Event-centric Composable Queue (ECQ), a basic building block of a new scalable composite event monitoring (CEM) framework, SCEMon. In particular, we focus on the scalability issues when large numbers of CEM queries and event sources exist in upcoming CEM environments. To address these challenges effectively, we take an event-centric sharing approach rather than dealing with queries and sources separately. ECQ is a shared queue, which stores incoming event instances of a primitive event class. ECQs are designed to facilitate efficient shared evaluations of multiple queries over very large volumes of event streams from numerous event sources. ECQs are composable and form a single shared network within which multiple queries are simultaneously evaluated. In this paper, we present efficient shared processing techniques operating on top of the proposed shared ECQ network. The performance evaluation shows that the proposed approach achieves a high level of scalability compared to conventional separate processing approaches in large-scale CEM environments.

VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls.

In this paper, we introduce a novel smartphone framework called VisitSense that automatically detects and predicts a smartphone user’s place visits from ambient radio to enable behavioral targeting for mobile ads in large shopping malls. VisitSense enables mobile app developers to adopt visit-pattern-aware mobile advertising for shopping mall visitors in their apps. It also benefits mobile users by allowing them to receive highly relevant mobile ads that are aware of their place visit patterns in shopping malls. To achieve the goal, VisitSense employs accurate visit detection and prediction methods. For accurate visit detection, we develop a change-based detection method to take into consideration the stability change of ambient radio and the mobility change of users. It performs well in large shopping malls where ambient radio is quite noisy and causes existing algorithms to easily fail. In addition, we proposed a causality-based visit prediction model to capture the causality in the sequential visit patterns for effective prediction. We have developed a VisitSense prototype system, and a visit-pattern-aware mobile advertising application that is based on it. Furthermore, we deploy the system in the COEX Mall, one of the largest shopping malls in Korea, and conduct diverse experiments to show the effectiveness of VisitSense.

The content-aware caching for cooperative transcoding proxies

The Web is rapidly increasing its reach beyond the desktop to various devices and the transcoding proxy is appeared to support web services efficiently. Recently, the cooperative transcoding proxy architecture is proposed to improve the system performance to cope with the scalability problem of a stand-alone transcoding proxy. However, because of the multiple versions, the communication protocol of the cooperative caches is very complex and causes additional delay to find best version for a requested object. In this paper, we propose efficient cooperative transcoding proxy architecture which uses the content-aware caching. The main purpose of the proposed system is simplifying the communication protocol of cooperative caches. We associates a home proxy for each URL and the home proxy is responsible for transcoding and maintaining multiple version of an URL. This mechanism reduces the amount of messages exchanged and communication latency involved. To prevent the hot-spot problem, each proxy cache has the private cache which stores the recently requested objects. We examine the performance of the proposed system by using trace based simulation with Simjava and show the effective enhancement of the cooerative transcoding proxy system.