Evaluating continuous nearest neighbor queries for streaming time series via pre-fetching

Abstract

For many applications, it is important to quickly locate the nearest neighbor of a given time series. When the given time series is a streaming one, nearest neighbors may need to be found continuously at all time positions. Such a standing request is called a continuous nearest neighbor query. This paper seeks fast evaluation of continuous queries on large databases. The initial strategy is to use the result of one evaluation to restrict the search space for the next. A more fundamental idea is to extend the existing indexing methods, used in many traditional nearest neighbor algorithms, with pre-fetching. Specifically, pre-fetching is to predict the next value of the stream before it arrives, and to process the query as if the predicted value were the real one in order to load the needed index pages and time series into the allocated cache memory. Furthermore, if the pre-fetched candidates cannot fit into the cache memory, they are stored in a sequential file to facilitate fast access to them. Experiments show that prefetching improves the response time greatly over the direct use of traditional algorithms, even if the caching provided by the operating system is taken into consideration.

On processing continuous frequent K-N-match queries for dynamic data over networked data sources

“…(5,8) (4,6.0) (5,9) (5,10) (f) Step [3] is increased by one. Since appear [3] equals 2 (i.e., n), data point 3 is inserted into S and h is increased by 1.…”

“…It is obvious that if the attributes of the objects do not significantly change, the changes of the similarities among objects and the query point are of highly likelihood to be insignificant. Therefore, not all changes will affect the results of queries, and there is much redundant work if we re-evaluate all queries every time a data point changes [8]. Based on this observation, we first introduce the concept of safe regions 2 which are defined as the intervals that the result of the query will not change as long as each attribute of each point is within its safe region.…”

Adaptive similarity search in streaming time series with sliding windows

“…Then, false alarms are discarded. The same authors have proposed two different approaches, based on pre-fetching [12,13]. Both the aforementioned research efforts examine the case of whole-match queries, where the data set is composed of static time series and the query is dynamic (changes over time).…”

Similar sequence matching supporting variable-length and variable-tolerance continuous queries on time-series data stream

“…Query sequences can be registered by many different users with different requirements on the lengths and tolerances. Nevertheless, existing results reported in the literature either support only fixed-length or fixed-tolerance continuous query sequences [9,14] or are unable to support a large number of query sequences with variable lengths or variable tolerances [10]. Other recent similar sequence matching methods reported in the data stream environment are only capable of handling one continuous query sequence [6,20].…”

“…There have been a number of efforts to handle the time-series data stored in databases [1,8,17,18,21,27]. Recently, the data stream has become of growing importance with new requirements due to advances in network technology and mobile/sensor devices in emerging ubiquitous environments [6,9,10,14,20]. A data stream is a sequence of data entries that continuously arrive in a sequential order [2,19].…”