Peiquan Jin

CCF-LRU: a new buffer replacement algorithm for flash memory

NAND flash memory has been widely used for storage in embedded systems and recently in enterprise computing environment, owing to its shock-resistance, nonvolatile, low energy consumption, and high I/O speed. In addition, flash memory has the characteristics of not-in-place update and asymmetric I/O costs among read, write, and erase operations, in which the cost of write/erase operations is much higher than that of read operation. Hence, the buffer replacement algorithms in flash-based systems should take the asymmetric I/O costs into account. Previous solutions to this issue, such as CFLRU and LRU-WSR, used a clean-first scheme to first evict clean pages from the buffer. Although they outperform the traditional LRU policy in performance, they do not consider the access frequency of clean page, which will consequently result in poor I/O performance. In order to solve this problem, we present a new buffering algorithm in this paper, called CCF-LRU, which enhances the previous CFLRU and LRU-WSR methods by differentiating clean pages into cold and hot ones, and evicting cold clean pages first and delaying the eviction of hot clean pages. We conduct a trace-driven experiment on a flash memory simulation environment, and use six types of synthesized traces as well as a real OLTP trace. The results show that the CCF-LRU algorithm outperforms LRU, CFLRU and LRUWSR in write count and runtime in all experiments. In particular, it provides more than 20% improvement over its competitors both in write count and runtime, when running the real OLTP trace.

CFDC: a flash-aware replacement policy for database buffer management

Flash disks are becoming an important alternative to conventional magnetic disks. Although accessed through the same interface by applications, flash disks have some distinguished characteristics that make it necessary to reconsider the design of the software to leverage their performance potential. This paper addresses this problem at the buffer management layer of database systems and proposes a flash-aware replacement policy that significantly improves and outperforms one of the previous proposals in this area.

AD-LRU: An efficient buffer replacement algorithm for flash-based databases

Flash memory has characteristics of out-of-place update and asymmetric I/O latencies for read, write, and erase operations. Thus, the buffering policy for flash-based databases has to consider those properties to improve the overall performance. This article introduces a new approach to buffer management for flash-based databases, called AD-LRU (Adaptive Double LRU), which focuses on improving the overall runtime efficiency by reducing the number of write/erase operations and by retaining a high buffer hit ratio. We conduct trace-driven experiments both in a simulation environment and in a real DBMS, using a real OLTP trace and four kinds of synthetic traces: random, read-most, write-most, and Zipf. We make detailed comparisons between our algorithm and the best-known competitor methods. The experimental results show that AD-LRU is superior to its competitors in most cases.

TISE: A Temporal Search Engine for Web Contents

In this paper, we present a temporal search engine supporting content time retrieval for Web pages, which is called TISE. The main purpose of TISE is to support the Web search on temporal information embedded in Web pages. Compared with commercial search engines such as Google and Baidu, and other temporal search prototypes, which mainly focus on the creation or crawled time of Web pages, our system concentrates on the extraction and search on content time of Web pages, and can provide more meaningful time-based search facilities, such as temporal relation query. In detail, TISE is based on a unified temporal ontology of Web pages, in which different types of time are defined. We introduce a new type of time ldquoprimary timerdquo to denote the most appropriate time describing the content of a Web page. After an overview of the general features of TISE, we discuss the architecture of TISE and some key modules. And finally, experiment results and analysis is conducted based on fix types of temporal-text queries on TISE and www.baidu.com. The experiment results show that TISE is more efficient when processing temporal-text Web queries.

A flexible simulation environment for flash-aware algorithms

In this paper, we present a flexible simulation environment for the performance evaluation of flash-aware algorithms, which is called Flash-DBSim. The main purpose of Flash-DBSim is to provide a configurable virtual flash disk for upper systems, such as file system and DBMS, so that the algorithms in those systems can be easily evaluated on different types of flash disks. Moreover, it also offers a prototyping environment for those algorithms inside flash disk, e.g. the algorithms for garbage collection or wear-leveling. After an overview of the general features of Flash-DBSim, we discuss the architecture of Flash-DBSim. And finally, a case study of Flash-DBSims demonstration is presented.

An approach for image retrieval based on visual saliency

Considering the gap between low-level image features and the high-level semantic concept in content-based image retrieval (CBIR), a new approach is proposed for image retrieval based on visual saliency, by analyzing the human visual perception process. Visual information is introduced as the new feature which reflects high-level semantic concept objectively. First, the visual saliency model for image retrieval is established. The saliency features of intensity, color and texture are calculated. Second, integrated global saliency map is synthesized and its statistic histogram is used as a new feature in image retrieval. Finally, the similarity of color images is computed by combining the color feature and the histogram of integrated saliency map. Results of experiments show that our approach improves retrieval precision and recall when compared with the classical color feature approach.

Extracting Focused Locations for Web Pages

Most Web pages contain location information, which can be used to improve the effectiveness of search engines. In this paper, we concentrate on the focused locations, which refer to the most appropriate locations associated with Web pages. Current algorithms suffer from the ambiguities among locations, as many different locations share the same name (known as GEO/GEO ambiguity), and some locations have the same name with non-geographical entities such as person names (known as GEO/NON-GEO ambiguity). In this paper, we first propose a new algorithm named GeoRank, which employs a similar idea with PageRank to resolve the GEO/GEO ambiguity. We also introduce some heuristic rules to eliminate the GEO/NON-GEO ambiguity. After that, an algorithm with dynamic parameters to determine the focused locations is presented. We conduct experiments on two real datasets to evaluate the performance of our approach. The experimental results show that our algorithm outperforms the state-of-the-art methods in both disambiguation and focused locations determination.

An adaptive block-set based management for large-scale flash memory

With rapid increase of the capacity of flash-memory storage systems, it becomes critical to provide efficient management for large-scale flash-memory. Compared with FTL (Flash Translation Layer), NFTL (NAND Flash Translation Layer) provides less main-memory space requirements for large-scale flash memory. However, because each replacement block is exclusively used by a logical block, NFTL exhibits poor space utilization of flash memory. In this paper, we present an adaptive block-set based flash memory management. The presented scheme adopts shared and exclusive replacement blocks, and allocates replacement blocks according to the update loads of logical blocks. The experimental results show that the presented scheme yields a better performance in garbage collection than NFTL and FAST (fully associative sector translation), keeping space utilization of flash memory at high level.

Exploiting temporal information in Web search

Time plays important roles in Web search, because most Web pages contain temporal information and a lot of Web queries are time-related. How to integrate temporal information in Web search engines has been a research focus in recent years. However, traditional search engines have little support in processing temporal-textual Web queries. Aiming at solving this problem, in this paper, we concentrate on the extraction of the focused time for Web pages, which refers to the most appropriate time associated with Web pages, and then we used focused time to improve the search efficiency for time-sensitive queries. In particular, three critical issues are deeply studied in this paper. The first issue is to extract implicit temporal expressions from Web pages. The second one is to determine the focused time among all the extracted temporal information, and the last issue is to integrate focused time into a search engine. For the first issue, we propose a new dynamic approach to resolve the implicit temporal expressions in Web pages. For the second issue, we present a score model to determine the focused time for Web pages. Our score model takes into account both the frequency of temporal information in Web pages and the containment relationship among temporal information. For the third issue, we combine the textual similarity and the temporal similarity between queries and documents in the ranking process. To evaluate the effectiveness and efficiency of the proposed approaches, we build a prototype system called Time-Aware Search Engine (TASE). TASE is able to extract both the explicit and implicit temporal expressions for Web pages, and calculate the relevant score between Web pages and each temporal expression, and re-rank search results based on the temporal-textual relevance between Web pages and queries. Finally, we conduct experiments on real data sets. The results show that our approach has high accuracy in resolving implicit temporal expressions and extracting focused time, and has better ranking effectiveness for time-sensitive Web queries than its competitor algorithms.

Exploiting location information for Web search

Most Web pages contain location information, which are usually neglected by traditional search engines. Queries combining location and textual terms are called as spatial textual Web queries. Based on the fact that traditional search engines pay little attention in the location information in Web pages, in this paper we study a framework to utilize location information for Web search. The proposed framework consists of an offline stage to extract focused locations for crawled Web pages, as well as an online ranking stage to perform location-aware ranking for search results. The focused locations of a Web page refer to the most appropriate locations associated with the Web page. In the offline stage, we extract the focused locations and keywords from Web pages and map each keyword with specific focused locations, which forms a set of pairs. In the second online query processing stage, we extract keywords from the query, and computer the ranking scores based on location relevance and the location-constrained scores for each querying keyword. The experiments on various real datasets crawled from nj.gov, BBC and New York Time show that the performance of our algorithm on focused location extraction is superior to previous methods and the proposed ranking algorithm has the best performance w.r.t different spatial textual queries.