Foreword to invited papers issue

Germany). These papers were recommended by the program chairs and program committees of the respective conferences as the best papers to be invited for submission, and they were selected for publication after a full peer-review process according to TODS policies. The first three invited articles in this issue are selected from SIGMOD 2012. Zaniolo introduces a new language (XSeq) for processing complex event queries over XML streams, discusses the language implementation over visibly pushdown automata, and presents formal semantics of the language, complexity analysis, and performance evaluation over multiple settings. studies the problem of thinning related to visualizing map datasets at multiple scales (i.e., given a region at a particular zoom level, the goal is to return a small number of records to represent the region). They describe several novel algorithms and present analyses and experimentation demonstrating the trade-offs among the algorithms. The third article, " XLynx—An FPGA-Based XML Filter for Hybrid XQuery Processing " by Jens Teubner, Louis Woods, and Chongling Nie, addresses the problem of speeding up Xquery processing by focusing on the task of XML projection. They propose a novel method using field-programmable gate-arrays (FPGAs), called the skeleton automata, which is both highly expressive and easily reconfigurable. The next three articles in this issue are selected from PODS 2012. The article " The Complexity of Regular Expressions and Property Paths in SPARQL " by Katja Losemann and Wim Martens formalizes the W3C semantics of property paths and studies the complexity of two basic problems related to query evaluation on graphs. The second article, " Static Analysis and Optimization of Semantic Web Queries " by studies the optimization of SPARQL queries with a special focus on the Optionality feature in SPARQL and presents extensive results including a characterization of query answers and various complexity results for query evaluation, subsumption, and equivalence. summary of a dataset is a compression that allows one to estimate various statistical quantities about the dataset. This article considers mergeable summaries of datasets where a summary of the combined dataset can be obtained from the summaries of two datasets and presents several novel results and well-designed experiments supporting the theoretical results on the mergeability of different statistical quantities.