Kian-Lee Tan

Bio: Kian-Lee Tan is an academic researcher from National University of Singapore. The author has contributed to research in topics: Query optimization & Scalability. The author has an hindex of 65, co-authored 465 publications receiving 17755 citations. Previous affiliations of Kian-Lee Tan include University of California, Santa Barbara & Fudan University.

High H2 uptake by alkali-doped carbon nanotubes under ambient pressure and moderate temperatures

Abstract: Lithium- or potassium-doped carbon nanotubes can absorb approximately 20 or approximately 14 weight percent of hydrogen at moderate (200 degrees to 400 degrees C) or room temperatures, respectively, under ambient pressure. These values are greater than those of metal hydride and cryoadsorption systems. The hydrogen stored in the lithium- or potassium-doped carbon nanotubes can be released at higher temperatures, and the sorption-desorption cycle can be repeated with little decrease in the sorption capacity. The high hydrogen-uptake capacity of these systems may be derived from the special open-edged, layered structure of the carbon nanotubes made from methane, as well as the catalytic effect of alkali metals.

Private queries in location based services: anonymizers are not necessary

Abstract: Mobile devices equipped with positioning capabilities (e.g., GPS) can ask location-dependent queries to Location Based Services (LBS). To protect privacy, the user location must not be disclosed. Existing solutions utilize a trusted anonymizer between the users and the LBS. This approach has several drawbacks: (i) All users must trust the third party anonymizer, which is a single point of attack. (ii) A large number of cooperating, trustworthy users is needed. (iii) Privacy is guaranteed only for a single snapshot of user locations; users are not protected against correlation attacks (e.g., history of user movement).We propose a novel framework to support private location-dependent queries, based on the theoretical work on Private Information Retrieval (PIR). Our framework does not require a trusted third party, since privacy is achieved via cryptographic techniques. Compared to existing work, our approach achieves stronger privacy for snapshots of user locations; moreover, it is the first to provide provable privacy guarantees against correlation attacks. We use our framework to implement approximate and exact algorithms for nearest-neighbor search. We optimize query execution by employing data mining techniques, which identify redundant computations. Contrary to common belief, the experimental results suggest that PIR approaches incur reasonable overhead and are applicable in practice.

Efficient Progressive Skyline Computation

Abstract: In this paper, we focus on the retrieval of a set of interesting answers called the skyline from a database. Given a set of points, the skyline comprises the points that are not dominated by other points. A point dominates another point if it is as good or better in all dimensions and better in at least one dimension. We present two novel algorithms, Bitmap and Index, to compute the skyline of a set of points. Unlike most existing algorithms that require at least one pass over the dataset to return the rst interesting point, our algorithms progressively return interesting points as they are identi ed. Our performance study further shows that the proposed algorithms provide quick initial response time with Index being superior in most cases.

BLOCKBENCH: A Framework for Analyzing Private Blockchains

Abstract: Blockchain technologies are taking the world by storm. Public blockchains, such as Bitcoin and Ethereum, enable secure peer-to-peer applications like crypto-currency or smart contracts. Their security and performance are well studied. This paper concerns recent private blockchain systems designed with stronger security (trust) assumption and performance requirement. These systems target and aim to disrupt applications which have so far been implemented on top of database systems, for example banking, finance and trading applications. Multiple platforms for private blockchains are being actively developed and fine tuned. However, there is a clear lack of a systematic framework with which different systems can be analyzed and compared against each other. Such a framework can be used to assess blockchains' viability as another distributed data processing platform, while helping developers to identify bottlenecks and accordingly improve their platforms. In this paper, we first describe BLOCKBENCH, the first evaluation framework for analyzing private blockchains. It serves as a fair means of comparison for different platforms and enables deeper understanding of different system design choices. Any private blockchain can be integrated to BLOCKBENCH via simple APIs and benchmarked against workloads that are based on real and synthetic smart contracts. BLOCKBENCH measures overall and component-wise performance in terms of throughput, latency, scalability and fault-tolerance. Next, we use BLOCKBENCH to conduct comprehensive evaluation of three major private blockchains: Ethereum, Parity and Hyperledger Fabric. The results demonstrate that these systems are still far from displacing current database systems in traditional data processing workloads. Furthermore, there are gaps in performance among the three systems which are attributed to the design choices at different layers of the blockchain's software stack. We have released BLOCKBENCH for public use.

iDistance: An adaptive B+-tree based indexing method for nearest neighbor search

Abstract: In this article, we present an efficient Bp-tree based indexing method, called iDistance, for K-nearest neighbor (KNN) search in a high-dimensional metric space. iDistance partitions the data based on a space- or data-partitioning strategy, and selects a reference point for each partition. The data points in each partition are transformed into a single dimensional value based on their similarity with respect to the reference point. This allows the points to be indexed using a Bp-tree structure and KNN search to be performed using one-dimensional range search. The choice of partition and reference points adapts the index structure to the data distribution.We conducted extensive experiments to evaluate the iDistance technique, and report results demonstrating its effectiveness. We also present a cost model for iDistance KNN search, which can be exploited in query optimization.

Hydrogen-storage materials for mobile applications

Abstract: Mobility — the transport of people and goods — is a socioeconomic reality that will surely increase in the coming years. It should be safe, economic and reasonably clean. Little energy needs to be expended to overcome potential energy changes, but a great deal is lost through friction (for cars about 10 kWh per 100 km) and low-efficiency energy conversion. Vehicles can be run either by connecting them to a continuous supply of energy or by storing energy on board. Hydrogen would be ideal as a synthetic fuel because it is lightweight, highly abundant and its oxidation product (water) is environmentally benign, but storage remains a problem. Here we present recent developments in the search for innovative materials with high hydrogen-storage capacity.

“Bioinformatics” 특집을 내면서

Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

A survey of trust and reputation systems for online service provision

Abstract: Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision. The basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive a trust or reputation score, which can assist other parties in deciding whether or not to transact with that party in the future. A natural side effect is that it also provides an incentive for good behaviour, and therefore tends to have a positive effect on market quality. Reputation systems can be called collaborative sanctioning systems to reflect their collaborative nature, and are related to collaborative filtering systems. Reputation systems are already being used in successful commercial online applications. There is also a rapidly growing literature around trust and reputation systems, but unfortunately this activity is not very coherent. The purpose of this article is to give an overview of existing and proposed systems that can be used to derive measures of trust and reputation for Internet transactions, to analyse the current trends and developments in this area, and to propose a research agenda for trust and reputation systems.

Internet of things: Vision, applications and research challenges

Abstract: The term ‘‘Internet-of-Things’’ is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. Internet-of-Things envisions a future in which digital and physical entities can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this article, we present a survey of technologies, applications and research challenges for Internetof-Things.