Efficient motif discovery for large scale time series in healthcare

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Efficient Motif Discovery for Large-Scale Time Series in Healthcare
ABSTRACT:
Analyzing time series data can reveal the temporal behavior of the underlying
mechanism producing the data. Time series motifs, which are similar subsequences or
frequently occurring patterns, have significant meanings for researchers especially in
medical domain. With the fast growth of time series data, traditional methods for motif
discovery are inefficient and not applicable to large-scale data. This work proposes an
efficient Motif Discovery method for Large-scale time series (MDLats). By computing
standard motifs, MDLats eliminates a majority of redundant computation in the related arts
and reuses existing information to the maximum. All the motif types and subsequences are
generated for subsequent analysis and classification. Our system is implemented on a
Hadoop platform and deployed in a hospital for clinical electrocardiography classification.
The experiments on real-world healthcare data show that MDLats outperform the state-of-
the-art methods even in large time series.
INTRODUCTION
LARGE AMOUNTS of data are being collected from scientific experiments, business
operations, and social media in the big data era . As a special case, thousands of research
labs and commercial enterprises are generating large amounts of temporal data. For
example, many research hospitals have trillions of data points of electrocardiography
(ECG) data; Twitter collects 170 million temporal data every minute and serves up 200
million queries per day. These temporal data are sequentially collected over time, and are
called time series. A time series is a sequence of real numbers where each number
represents a value at a point of time. Mapping to the physical world, a time series can
represent network flow, exchange rates, or weather conditions over time.

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EXISTING SYSTEM
Traditional time series data mining algorithms cannot handle large-scale time series
efficiently. Owing to its high time consumption, similarity search used in such algorithms
becomes their bottleneck for classification, clustering, motif discovery, and anomaly
detection. Time series motifs, which are similar subsequences or frequently occurring
patterns, have significant meanings for researchers especially in healthcare domain.
DisADVANTAGE OF Existing SYSTEM
 Due to their poor scalability, existing methods on discovering motifs cannot achieve
satisfactory efficiency and accuracy when facing large-scale time series data
PROPOSED SYSTEM
In Proposed System Motif Discovery method for Large-scale time series data
(MDLats) by combining the advantages of both exact and approximate methods. It
balances the efficiency and accuracy by computing standard motifs, based on which the
final motifs, including all the similar subsequences,are discovered adaptively. We have
implemented MDLats on Hadoop. Experiments on public University of California, Riverside
(UCR), time series data and random walks dataset show that it can scale to a very large
size and well outperforms competitors in processing time, precision, and recall. Its real-
world healthcare application on ECG classification achieves over 95% precision and recall.
ADVANTAGE OF PROPOSED SYSTEM
 It improves the efficiency for motif discovery while retaining accuracy. By computing
standard motifs, it can reduce majority of redundant computation, and reuse
existing information to the maximum by exploiting the relation between existing ata
and newly arrived ones.
 It can generate all the motifs, including not only the most similar pairs of

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subsequences as commonly done in existing methods but also all the similar
subsequences, which provide additional information for time series analysis.
 It is adaptive to different length of motifs, and can avoid a full scan in the
preprocessing. It is extremely efficient when the time series are regular and the
fluctuations in them are sparse. Generally, both ECG and network flow data enjoy
this feature.
 It is scalable and can be deployed in Hadoop for parallel computing.
ARCHITECTURE:

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HARDWARE REQUIREMENTS:
 System : Pentium IV 2.4 GHz.
 Hard Disk : 40 GB.
 Floppy Drive : 44 Mb.
 Monitor : 15 VGA Colour.
SOFTWARE REQUIREMENTS:
 Operating system : Windows 7.
 Coding Language : Java 1.7 ,Hadoop 0.8.1
 Database : MySql 5
 IDE : Eclipse

Efficient motif discovery for large scale time series in healthcare

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