Fast Algorithms for a Class of Temporal Range Queries

We consider the problem of querying large scale multidimensional time series data to discover events of interest, test and validate hypotheses, or to associate temporal patterns with specific events. This type of data currently dominate most other types of available data, and will very likely become even more prevalent in the future given the current trends in collecting time series of business, scientific, demographic, and simulation data. The ability to explore such collections interactively, even at a coarse level, will be critical in discovering the information and knowledge embedded in such collections. We develop indexing techniques and search algorithms to efficiently handle temporal range value querying of multidimensional time series data. Our indexing uses linear space data structures that enable the handling of queries in I/O time that is essentially the same as that of handling a single time slice, assuming the availability of a logarithmic number of processors as a function of size of the temporal window. A data structure with provably good bounds is also presented for the case when the number of multidimensional objects is relatively small. These techniques improve significantly over standard techniques for either serial or parallel processing, and are evaluated by extensive experimental results that confirm their superior performance.

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“…We should note that special cases of our problem were addressed in [11] in the case of internal memory algorithms.…”

Section: A Possible Approaches Based On Standard Techniquesmentioning

confidence: 99%

Temporal range exploration of large scale multidimensional time series data

JaJa¹,

Kim²,

Wang³

Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004.

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Techniques for Indexing and Querying Temporal Observations for a Collection of Objects

Shi

JáJá

2004

Algorithms and Computation

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We consider the problem of dynamically indexing temporal observations about a collection of objects, each observation consisting of a key identifying the object, a list of attribute values and a timestamp indicating the time at which these values were recorded. We m a k e no assumptions about the rates at which these observations are collected, nor do we assume that the various objects have about the same number of observations. We d e v elop indexing structures that are almost linear in the total number of observations available at any g i v en time instant, and that support dynamic insertions in polylogarithmic time. Moreover, these structures allow the quick handling of queries to identify objects whose attribute values fall within a certain range at every time instance of a speci ed time interval. Provably good bounds are established.

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Fast Algorithms for a Class of Temporal Range Queries

Cited by 2 publications

References 21 publications

Temporal range exploration of large scale multidimensional time series data

Temporal range exploration of large scale multidimensional time series data

Techniques for Indexing and Querying Temporal Observations for a Collection of Objects

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