A MapReduce Based Distributed LSI for Scalable Information Retrieval

Latent Semantic Indexing (LSI) has been widely used in information retrieval due to its efficiency in solving the problems of polysemy and synonymy. However, LSI is notably a computationally intensive process because of the computing complexities of singular value decomposition and filtering operations involved in the process. This paper presents MR-LSI, a MapReduce based distributed LSI algorithm for scalable information retrieval. The performance of MR-LSI is first evaluated in a small scale experimental cluster environment, and subsequently evaluated in large scale simulation environments. By partitioning the dataset into smaller subsets and optimizing the partitioned subsets across a cluster of computing nodes, the overhead of the MR-LSI algorithm is reduced significantly while maintaining a high level of accuracy in retrieving documents of user interest. A genetic algorithm based load balancing scheme is designed to optimize the performance of MR-LSI in heterogeneous computing environments in which the computing nodes have varied resources

Bright 22 μ\mum Excess Candidates from WISE All-Sky Catalog and Hipparcos Main Catalog

In this paper we present a catalog which includes 141 bright candidates ($\leq10.27$ mag, V band) showing the infrared (IR) excess at 22 $\mu$m. Of which, 38 stars are known IR excess stars or disk, 23 stars are double or multiple stars and 4 are Be stars. While the remaining more than 70 stars are identified as the 22 $\mu$m excess candidates in our work. The criterion of selecting candidates is $K_s-[22]_{\mu m}$. All these candidates are selected from \emph{WISE} All-sky data cross-correlated with \emph{Hipparcos} Main Catalog and the likelihood-ratio technique is employed. Considering the effect of background, we introduce the \emph{IRAS} 100 $\mu$m level to exclude the high background. We also estimated the coincidence probability of these sources. In addition, we presented the optical to mid-infrared SEDs and optical images of all the candidates, and gave the observed optical spectra of 6 stars with NAOC's 2.16-m telescope. To measure for the dust amount around each star, the fractional luminosity is also provided. We also test whether our method of selecting IR excess stars can be used to search for extra-solar planets, we cross-matched our catalog with known IR-excess stars having planets but none is matched. Finally, we give the fraction of stars showing IR-excess for different spectral type of main-sequence stars.Comment: 45 pages, 16 figures, 4 tables. Accepted for publication in ApJ