SSTD 2019

Two of our papers have been accepted for publication at the 16th International Symposium on Spatial and Temporal Databases (SSTD 2019), which will take place on the 19th – 21st of August in Vienna, Austria.

First paper titled “Local Pair and Bundle Discovery over Co-Evolving Time Series” introduces the problem of detecting locally similar pairs and groups of time series, thus indicating common local patterns and trends. We present a sweep line algorithm and an improved, filter-verification technique that only examines candidate matches at judiciously chosen checkpoints across time. An experimental evaluation of our methods against real-world and synthetic datasets demonstrates a speed-up in execution time by an order of magnitude over the baseline.

Second paper titled “Scalable temporal clique enumeration” introduces the problem of enumeration of all k-sized subsets of temporal events that mutually overlap at some point in query time window. We propose a specialized plane sweep approach that overcomes the efficiency bottlenecks of current methods which are based on 2-way interval join algorithms to enumerate temporal k-cliques. Additionally, we investigate how precomputed checkpoints can be used to further improve the efficiency of our plane sweep algorithm. Our experimental results demonstrate that our approach outperforms the state of the art by a wide margin and that our checkpointing strategies are effective.

For those of you who will attend the conference, we would be happy to meet you there!