ITR Collaborative Research: Compressed Search and Retrieval for Very Large Text and Image Repositories

我们建议开发高效的技术来搜索和检索非常大的文本和图像存储库。当压缩基于一系列依赖于排序上下文的算法时，我们将开发算法来对文本和图像执行压缩域的精确和近似模式匹配。我们还将研究基于上下文的预测图像压缩方案的压缩模式搜索，尽管这些算法使用的上下文不是排序的上下文。我们将开发搜索感知的压缩方案，该方案将支持直接对压缩数据进行压缩域搜索，对压缩数据进行最小程度的解压缩或不解压缩。我们还将开发软件工具，并创建一个综合的全球压缩/搜索实用网站，以便无损压缩文本和图像，并直接以压缩形式有效地搜索大量收藏。我们将开发使用压缩关键字或倒排词典的压缩域搜索引擎，以加快对TB级文本和图像存储库的搜索操作。这项研究拟议活动的更广泛影响将是有效利用存储、计算和通信资源。研究生将接受研究方面的培训，最先进的数据压缩和信息检索技术知识将转移到课堂上

We propose to develop efficient techniques for search and retrieval for very large text and image repositories. We will develop algorithms to perform compressed domain exact and approximate pattern matching on text and images, when the compression is based on the family of algorithms that depend on sorted contexts. We will also investigate compressed pattern search for context-based predictive image compression schemes, although the contexts used by these algorithms are not sorted contexts. We will develop search-aware compression schemes that will support compressed-domain search directly on the compressed data with minimal or no decompression of the compressed data. We will also develop software tools and create an integrated global compression/search utility website for lossless compression of text and images and for efficient search of large collections directly in their compressed form. We will develop compressed domain search engines using compressed keywords or inversion dictionaries to expedite search operations for terabyte scale text and image repositories. The broader impact of the proposed activity of this research will be efficient utilization of storage, computation and communication resources. Graduate students will be trained in doing research and state-of-the-art knowledge in data compression and information retrieval technology will be transferred to classrooms