The current state of the art in subgraph isomorphism solving involves using degree as a value-ordering heuristic to direct backtracking search. Such a search makes a heavy commitment to the first branching choice, which is often incorrect. To mitigate this, we introduce and evaluate a new approach, which we call "solution-biased search". By combining a slightly-random value-ordering heuristic, rapid restarts, and nogood recording, we design an algorithm which instead uses degree to direct the proportion of search effort spent in different subproblems. This increases performance by two orders of magnitude on satisfiable instances, whilst not affecting performance on unsatisfiable instances. This algorithm can also be parallelised in a very simple but effective way: across both satisfiable and unsatisfiable instances, we get a further speedup of over thirty from thirty-six cores, and over one hundred from ten distributed-memory hosts. Finally, we show that solution-biased search is also suitable for optimisation problems, by using it to improve two maximum common induced subgraph algorithms.

PrefacePeople shape societies. They are linked to each other by family ties and networks with social, economic and religious dimensions. People live together in households and form communities. Some own a house, land and other properties, often related to their profession. And all this is in continuous change. People are born, marry, have children and die, and they change houses and addresses, and build careers. For the study of a society in all aspects, people are at the heart of the problem and should be known in the context of their complex relationships. Even today, it is not easy to get this information in an all-enfolding way, but for populations in the past, it is a real challenge. And that is what this book is about. The book addresses the problems that are encountered, and solutions that have been proposed, when we aim to identify people and to reconstruct populations under conditions where information is scarce, ambiguous, fuzzy and sometimes erroneous.It is not a single discipline that is involved in such an endeavour. Historians, social scientists, and linguists represent the humanities through their knowledge of the complexity of the past, the limitations of sources and the possible interpretations of information. The availability of big data from digitised archives and the need of complex analyses to identify individuals require the involvement of computer scientists. With contributions from all these fields, often in direct cooperation, this book is at the heart of digital humanities and hopefully a source of inspiration for future investigations.The process from handwritten registers to a reconstructed digitised population has three major phases which shape the three sections of this book. The first phase is that of data transcription and digitisation while structuring the information in a meaningful and efficient way. Little of this phase can be automated. With archives that comprise easily tens of millions of records, the help of volunteers for transcription and digitisation is indispensable, but requires a rigorous management. Experiences from Denmark demonstrate the complexity of this task in Chap. 1. Spelling variation, aliases, abbreviations, errors and typos all generate difficulties in further processing and require data cleaning. Similarity measures can be helpful to v

No abstract