Exploring enormous state graphs represented implicitly by ordered binary decision diagrams (OBDDs) is one of the most successful applications of OBDDs. However, our worst-case analysis of implicit graph representations by OBDDs shows that there are cases where OBDD representations are not optimal and require more space than adjacency lists. As an improvement, we propose a new type of BDDs, called Patricia BDDs, which are capable of implicit representation of graphs while their worst-case sizes are kept equal or less than adjacency lists and OBDDs.

As one of the popular social media that many people turn to in recent years, collaborative encyclopedia Wikipedia provides information in a more “Neutral Point of View” way than others. Towards this core principle, plenty of efforts have been put into collaborative contribution and editing. The trajectories of how such collaboration appears by revisions are valuable for group dynamics and social media research, which suggest that we should extract the underlying derivation relationships among revisions from chronologically-sorted revision history in a precise way. In this paper, we propose a revision graph extraction method based on supergram decomposition in the document collection of near-duplicates. The plain text of revisions would be measured by its frequency distribution of supergram, which is the variable-length token sequence that keeps the same through revisions. We show that this method can effectively perform the task than existing methods.

Providing various assistances for design modifications on HDL source codes is important for design reuse and quick design cycle in VLSI CAD. Program slicing is a software-engineering technique for analyzing, abstracting, and transforming programs. We show algorithms for extracting/removing behaviors of specified signals in VHDL descriptions. We also describe a VHDL slicing system and show experimental results of efficiently extracting components from VHDL descriptions.