Inverted index maintenance strategy for flashSSDs: Revitalization of in-place index update strategy

An inverted index is a core data structure of Information Retrieval systems, especially in search engines. Since the search environments have become more dynamic, many on-line index maintenance strategies have been proposed. Previous strategies were designed for HDDs. Consequently, in order to avoid expensive random access cost, Merge-based strategies have been preferred to In-place index update strategies on HDDs. However, flashSSDs have become solid alternatives to HDDs. FlashSSDs currently are adopted in a wide range of areas due to their superior features such as the short access latency, energy efficiency, and high bandwidth. In this article, we first reexamined potentials of In-place index update strategies on flashSSDs. Thanks to the insignificant access latency of flashSSDs, we discovered that In-place index update strategies outperform Merge-based strategies, since In-place index update strategies generate much less amount of I/O than Merge-based strategies despite inducing frequent random accesses. Based on this discovery, we suggest a new inverted index maintenance strategy based on an In-place index update strategy for flashSSDs, called Multipath Flash In-place Strategy (MFIS). To enhance the index maintenance performance, MFIS stores the posting list of each term non-contiguously and exploits the internal parallelism of flashSSDs. Thus, MFIS not only induces the minimum amount of I/O but also utilizes the maximum bandwidth of flashSSDs. Furthermore, MFIS is designed to show high query processing performance by utilizing the internal parallelism of flashSSDs even though the posting list of each term is stored non-contiguously. In our experiments, the index maintenance performance of MFIS was considerably better than other previous maintenance strategies. The index maintenance performance was up to 14.93, 4.04, 5.12, and 2.33 times higher than Merge-based strategies such as Immediate Merge, Geometric Partitioning, Hybrid, and SSD-aware Hybrid, respectively. The query processing performance of MFIS was up to 1.62 times higher than non-contiguous In-place. In addition, MFIS showed almost the best query processing performance as Merge-based strategies did. In conclusion, MFIS is the best on-line inverted index maintenance strategy on flashSSDs in terms of both index maintenance and query processing performance.     

A comparison of pivot selection techniques for permutation-based indexing

Recently, permutation based indexes have attracted interest in the area of similarity search. The basic idea of permutation based indexes is that data objects are represented as appropriately generated permutations of a set of pivots (or reference objects). Similarity queries are executed by searching for data objects whose permutation representation is similar to that of the query, following the assumption that similar objects are represented by similar permutations of the pivots. In the context of permutation-based indexing, most authors propose to select pivots randomly from the data set, given that traditional pivot selection techniques do not reveal better performance. However, to the best of our knowledge, no rigorous comparison has been performed yet. In this paper we compare five pivot selection techniques on three permutation-based similarity access methods. Among those, we propose a novel technique specifically designed for permutations. Two significant observations emerge from our tests. First, random selection is always outperformed by at least one of the tested techniques. Second, there is no technique that is universally the best for all permutation-based access methods; rather different techniques are optimal for different methods. This indicates that the pivot selection technique should be considered as an integrating and relevant part of any permutation-based access method.     

Topic-centric and semantic-aware retrieval system for internet of things

The Internet of things (IoT) has been considered as one of the promising paradigms that can allow people and objects to seamlessly interact. So far, numerous applications and services have been proposed, such as retrieval service. The retrieval, however, faces a big challenge in IoT because the data belongs to different domains and user interaction with the surrounding environment is constrained. This paper proposes Acrost, a retrieval system based on topic discovery and semantic awareness in IoT environment. The initial contents with interesting information is obtained through the combination of two topic centric collectors. The metadata is extracted by aggregating regular expression-based and conditional random fields-based approaches. Moreover, the semantic-aware retrieval is achieved by parsing the query and ranking the relevance of contents. In addition, we present a case study on academic conference retrieval to validate the proposed approaches. Experimental results show that the proposed system can significantly improve the response time and efficiency of topic self-adaptive retrieval manner.     

A geometric framework for data fusion in information retrieval

Data fusion in information retrieval has been investigated by many researchers and a number of data fusion methods have been proposed. However, problems such as why data fusion can increase effectiveness and favorable conditions for the use of data fusion methods are poorly resolved at best. In this paper, we formally describe data fusion under a geometric framework, in which each component result returned from an information retrieval system for a given query is represented as a point in a multi-dimensional space. The Euclidean distance is the measure by which the effectiveness and similarity of search results are judged. This allows us to explain all component results and fused results using geometrical principles. In such a framework, score-based data fusion becomes a deterministic problem. Several interesting features of the centroid-based data fusion method and the linear combination method are discussed. Nevertheless, in retrieval evaluation, ranking-based measures are the most popular. Therefore, this paper investigates the relation and correlation between the Euclidean distance and several typical ranking-based measures. We indeed find that a very strong correlation exists between these. It means that the theorems and observations obtained using the Euclidean distance remain valid when ranking-based measures are used. The proposed framework enables us to have a better understanding of score-based data fusion and use score-based data fusion methods more precisely and effectively in various ways.     

Compositional strategies with generative scores

Generative processes have found broad application in the design of innovative musical forms but it is only comparatively recently through the affordances of modern digital technologies that composers have been able to explore integrating such processes in the real-time creation of musical scores. This paper explores some of these affordances as well as a variety of approaches to some of the key aesthetic and compositional challenges through the analysis of recent works by composers Georg Hajdu, Ryan Ross Smith, Jason Freeman, David Kim-Boyle, and Arne Eigenfeldt.     

基于Simhash的安全密文排序检索方案

针对密文检索中存在的计算量大、检索效率不高的问题，提出一种基于Simhash的安全密文排序检索方案。该方案基于Simhash的降维思想构建安全多关键词密文排序检索索引（SMRI），将文档处理成指纹和向量，利用分段指纹和加密向量构建B+树，并采用"过滤-精化"策略进行检索和排序，首先通过分段指纹的匹配进行快速检索，得到候选结果集；然后通过计算候选结果集与查询陷门的汉明距离和向量内积进行排序，带密钥的Simhash算法和安全k近邻（SkNN）算法保证了检索过程的安全性。实验结果表明，与基于向量空间模型（VSM）的方案相比，基于SMRI的排序检索方案计算量小，能节约时间和空间成本，检索效率高，适用于海量加密数据的快速安全检索。     

Building a text collection for Urdu information retrieval

Urdu is a widely spoken language in the Indian subcontinent with over 300 million speakers worldwide. However, linguistic advancements in Urdu are rare compared to those in other European and Asian languages. Therefore, by following Text Retrieval Conference standards, we attempted to construct an extensive text collection of 85 304 documents from diverse categories covering over 52 topics with relevance judgment sets at 100 pool depth. We also present several applications to demonstrate the effectiveness of our collection. Although this collection is primarily intended for text retrieval, it can also be used for named entity recognition, text summarization, and other linguistic applications with suitable modifications. Ours is the most extensive existing collection for the Urdu language, and it will be freely available for future research and academic education.     

基于跨语言词向量模型的蒙汉查询词扩展方法研究

跨语言信息检索指以一种语言为检索词,检索出用另一种或几种语言描述的一种信息的检索技术,是信息检索领域重要的研究方向之一。近年来,跨语言词向量为跨语言信息检索提供了良好的词向量表示,受到很多学者的关注。该文首先利用跨语言词向量模型实现汉文查询词到蒙古文查询词的映射,其次提出串联式查询扩展、串联式查询扩展过滤、交叉验证筛选过滤三种查询扩展方法对候选蒙古文查询词进行筛选和排序,最后选取上下文相关的蒙古文查询词。实验结果表明: 在蒙汉跨语言信息检索任务中引入交叉验证筛选方法对信息检索结果有很大的提升。