An answer to a query has a well-defined lineage expression (alternatively called how-provenance) that explains how the answer was derived. Recent work has also shown how to compute the lineage of a non-answer to a query. However, the cause of an answer or non-answer is a more subtle notion and consists, in general, of only a fragment of the lineage. In this paper, we adapt Halpern, Pearl, and Chockler's recent definitions of causality and responsibility to define the causes of answers and non-answers to queries, and their degree of responsibility . Responsibility captures the notion of degree of causality and serves to rank potentially many causes by their relative contributions to the effect. Then, we study the complexity of computing causes and responsibilities for conjunctive queries. It is known that computing causes is NP-complete in general. Our first main result shows that all causes to conjunctive queries can be computed by a relational query which may involve negation. Thus, causality can be computed in PTIME, and very efficiently so. Next, we study computing responsibility. Here, we prove that the complexity depends on the conjunctive query and demonstrate a dichotomy between PTIME and NP-complete cases. For the PTIME cases, we give a non-trivial algorithm, consisting of a reduction to the max-flow computation problem. Finally, we prove that, even when it is in PTIME, responsibility is complete for LOGSPACE, implying that, unlike causality, it cannot be computed by a relational query.

Software transactions have received significant attention as a way to simplify shared-memory concurrent programming, but insufficient focus has been given to the precise meaning of software transactions or their interaction with other language features. This work begins to rectify that situation by presenting a family of formal languages that model a wide variety of behaviors for software transactions. These languages abstract away implementation details of transactional memory, providing high-level definitions suitable for programming languages. We use small-step semantics in order to represent explicitly the interleaved execution of threads that is necessary to investigate pertinent issues.

Transactional memory provides a new concurrency control mechanism that avoids many of the pitfalls of lock-based synchronization. High-performance software transactional memory (STM) implementations thus far provide weak atomicity: Accessing shared data both inside and outside a transaction can result in unexpected, implementation-dependent behavior. To guarantee isolation and consistent ordering in such a system, programmers are expected to enclose all shared-memory accesses inside transactions.