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In this article, we present a novel word-based lossless compression algorithm for text files using a semi-static model. We named this method the ‘Multi-stream word-based compression algorithm (MWCA)’ because it stores the compressed forms of the words in three individual streams depending on their frequencies in the text and stores two dictionaries and a bit vector as side information. In our experiments, MWCA produces a compression ratio of 3.23 bpc on average and 2.88 bpc for files greater than 50 MB; if a variable length encoder such as Huffman coding is used after MWCA, the given ratios are reduced to 2.65 and 2.44 bpc, respectively. MWCA supports exact word matching without decompression, and its multi-stream approach reduces the search time with respect to single-stream algorithms. Additionally, the MWCA multi-stream structure supplies the reduction in network load by requesting only the necessary streams from the database. With the advantage of its fast compressed search feature and multi-stream structure, we believe that MWCA is a good solution, especially for storing and searching big text data. [ABSTRACT FROM AUTHOR]

In this study a new semistatic data compression model that has a fast coding process and that allows compressed pattern matching is introduced. The name of the proposed model is chosen as tagged word-based compression algorithm (TWBCA) since it has a word-based coding and word-based compressed matching algorithm. The model has two phases. In the first phase a dictionary is constructed by adding a phrase, paying attention to word boundaries, and in the second phase compression is done by using codewords of phrases in this dictionary. The first byte of the codeword determines whether the word is compressed or not. By paying attention to this rule, the CPM process can be conducted as word based. In addition, the proposed method makes it possible to also search for the group of consecutively compressed words. Any of the previous pattern matching algorithms can be chosen to use in compressed pattern matching as a black box. The duration of the CPM process is always less than the duration of the same process on the texts coded by Gzip tool. While matching longer patterns, compressed pattern matching takes more time on the texts coded by compress and end-tagged dense code (ETDC). However, searching shorter patterns takes less time on texts coded by our approach than the texts compressed with compress. Besides this, the compression ratio of our algorithm has a better performance against ETDC only on a file that has been written in Turkish. The compression performance of TWBCA is stable and does not vary over 6% on different text files. [ABSTRACT FROM AUTHOR]

We propose a new compression algorithm that compresses plain texts by using a dictionary-based model and a compressed string-matching approach that can be used with the compressed texts produced by this algorithm. The compression algorithm (CAFTS) can reduce the size of the texts to approximately 41% of their original sizes. The presented compressed string matching approach (SoCAFTS), which can be used with any of the known pattern matching algorithms, is compared with a powerful compressed string matching algorithm (ETDC) and a compressed string-matching tool (Lzgrep). Although the search speed of ETDC is very good in short patterns, it can only search for exact words and its compression performance differs from one natural language to another because of its word-based structure. Our experimental results show that SoCAFTS is a good solution when it is necessary to search for long patterns in a compressed document. [ABSTRACT FROM AUTHOR]