Elmar Haussmann

Optimized java binary and virtual machine for tiny motes

We have developed TakaTuka, a Java Virtual Machine optimized for tiny embedded devices such as wireless sensor motes. TakaTuka requires very little memory and processing power from the host device. This has been verified by successfully running TakaTuka on four different mote platforms. The focus of this paper is TakaTuka’s optimization of program memory usage. In addition, it also gives an overview of TakaTuka’s linkage with TinyOS and power management. TakaTuka optimizes storage requirements for the Java classfiles as well as for the JVM interpreter, both of which are expected to be stored on the embedded devices. These optimizations are performed on the desktop computer during the linking phase, before transferring the Java binary and the corresponding JVM interpreter onto a mote and thus without burdening its memory or computation resources. We have compared TakaTuka with the Sentilla, Darjeeling and Squawk JVMs.

More Informative Open Information Extraction via Simple Inference

Recent Open Information Extraction OpenIE systems utilize grammatical structure to extract facts with very high recall and good precision. In this paper, we point out that a significant fraction of the extracted facts is, however, not informative. For example, for the sentence The ICRW is a non-profit organization headquartered in Washington, the extracted fact a non-profit organization is headquartered in Washington is not informative. This is a problem for semantic search applications utilizing these triples, which is hard to fix once the triple extraction is completed. We therefore propose to integrate a set of simple inference rules into the extraction process. Our evaluation shows that, even with these simple rules, the percentage of informative triples can be improved considerably and the already high recall can be improved even further. Both improvements directly increase the quality of search on these triples.

A case for semantic full-text search

We discuss the advantages and shortcomings of full-text search on the one hand and search in ontologies/triple stores on the other hand. We argue that both techniques have an important quality missing from the other. We advocate a deep integration of the two, and describe the associated requirements and challenges.

WSDM Cup 2017: Vandalism Detection and Triple Scoring

The WSDM Cup 2017 was a data mining challenge held in conjunction with the 10th International Conference on Web Search and Data Mining (WSDM). It addressed key challenges of knowledge bases today: quality assurance and entity search. For quality assurance, we tackle the task of vandalism detection, based on a dataset of more than 82 million user-contributed revisions of the Wikidata knowledge base, all of which annotated with regard to whether or not they are vandalism. For entity search, we tackle the task of triple scoring, using a dataset that comprises relevance scores for triples from type-like relations including occupation and country of citizenship, based on about 10,000 human relevance judgments. For reproducibility sake, participants were asked to submit their software on TIRA, a cloud-based evaluation platform, and they were incentivized to share their approaches open source.

A Quality Evaluation of Combined Search on a Knowledge Base and Text

We provide a quality evaluation of KB+Text search, a deep integration of knowledge base search and standard full-text search. A knowledge base (KB) is a set of subject–predicate–object triples with a common naming scheme. The standard query language is SPARQL, where queries are essentially lists of triples with variables. KB+Text search extends this by a special occurs-with predicate, which can be used to express the co-occurrence of words in the text with mentions of entities from the knowledge base. Both pure KB search and standard full-text search are included as special cases. We evaluate the result quality of KB+Text search on three different query sets. The corpus is the full version of the English Wikipedia (2.4 billion word occurrences) combined with the YAGO knowledge base (26 million triples). We provide a web application to reproduce our evaluation, which is accessible via http://ad.informatik.uni-freiburg.de/publications.

Rethinking Java call stack design for tiny embedded devices

The ability of tiny embedded devices to run large feature-rich programs is typically constrained by the amount of memory installed on such devices. Furthermore, the useful operation of these devices in wireless sensor applications is limited by their battery life. This paper presents a call stack redesign targeted at an efficient use of RAM storage and CPU cycles by a Java program running on a wireless sensor mote. Without compromising the application programs, our call stack redesign saves 30% of RAM, on average, evaluated over a large number of benchmarks. On the same set of bench-marks, our design also avoids frequent RAM allocations and deallocations, resulting in average 80% fewer memory operations and 23% faster program execution. These may be critical improvements for tiny embedded devices that are equipped with small amount of RAM and limited battery life. However, our call stack redesign is equally effective for any complex multi-threaded object oriented program developed for desktop computers. We describe the redesign, measure its performance and report the resulting savings in RAM and execution time for a wide variety of programs.