We give a detailed algorithm for fast text compression. Our algorithm, related to the PPM method, simplifies the modeling phase by eliminating the escape mechanism and speeds up coding by using a combination of quasi-arithmetic coding and Rice coding. We provide details of the use of quasi-arithmetic code tables, and analyze their compression performance. Our Fast PPM method is shown experimentally to be almost twice as fast as the PPMC method, while giving comparable compression.

Reassembly of fragmented objects from a collection of randomly mixed fragments is a common problem in classical forensics. In this paper we address the digital forensic equivalent, i.e., reassembly of document fragments, using statistical modelling tools applied in data compression. We propose a general process model for automatically analyzing a collection fragments to reconstruct the original document by placing the fragments in proper order. Probabilities are assigned to the likelihood that two given fragments are adjacent in the original using context modelling techniques in data compression. The problem of finding the optimal ordering is shown to be equivalent to finding a maximum weight Hamiltonian path in a complete graph. Heuristics are designed and explored and implementation results provided which demonstrate the validity of the proposed technique. 1.

One of the most palpable drawbacks of XML can be seen in its excessive storage requirements. In this paper, we address this problem by proposing a syntactical XML compression scheme which makes use of probabilistic modeling of XML structure. Our compression scheme works sequentially and makes on-line processing of the data possible. We describe the current state of development of the prototype compressor and present some preliminary performance evaluation results. The compressor is designed to be extensible, and intended to serve as a platform for further research in the field of syntactical XML data compression. 1