Exploring Metadata Search Essentials for Scientific Data Management Scientific experiments and observations store massive amounts of data in various scientific file formats. Metadata, which describes the characteristics of the data, is commonly used to sift through massive datasets in order to locate data of interest to scientists. Several indexing data structures (such as hash tables, trie, selfbalancing search trees, sparse array, etc.) have been developed as part of efforts to provide an efficient method for locating target data. However, efficient determination of an indexing data structure remains unclear in the context of scientific data management, due to the lack of investigation on metadata, metadata queries, and corresponding data structures. In this study, we perform a systematic study of the metadata search essentials in the context of scientific data management. We study a realworld astronomy observation dataset and explore the characteristics of the metadata in the dataset. We also study possible metadata queries based on the discovery of the metadata characteristics and evaluate different data structures for various types of metadata attributes. Our evaluation on realworld dataset suggests that trie is a suitable data structure when prefixsuffix query is required, otherwise hash table should be used. We conclude our study with a summary of our findings. These findings provide a guideline and offers insights in developing metadata indexing methodologies for scientific applications.

Compressed Data Structures for Astronomical ContentAware Resource Search We introduce an efficient approach that aims at supporting contentbased queries on the Chilean Virtual Observatory. In particular we are interested in retrieving relevant information from virtualobservatory tables. This introduces several challenges that make the informationretrieval process harder. We define an algorithm that uses a compressed data structure to obtain the count of the number of occurrences of a string query within each column of a table. This kind of query has been used in the literature for faceted and semantic search as well as for retrieving information from web tables. This is in order to improve search effectiveness. We show that using only 15%xe2x80x9325% the space of a table our approach contains the table data (and hence the table can be deleted) and is able to answer queries efficiently in a few milliseconds.