Finding Motifs in a Database of Shapes

Xi, Xiaopeng; Keogh, Eamonn; Li, Wei; Mafra‐Neto, Agenor

doi:10.1137/1.9781611972771.23

Cited by 35 publications

(25 citation statements)

References 35 publications

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“…Xi et al do consider "Finding Motifs in a Database of Shapes" [27]. However, they make two critical assumptions that are not true in our case (or in general): that the individual shapes can be perfectly extracted, and that all shapes can be represented by a closed contour.…”

Section: Discussionmentioning

confidence: 96%

Mining Historical Documents for Near-Duplicate Figures

Rakthanmanon

Zhu

Keogh

2011

2011 IEEE 11th International Conference on Data Mining

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Abstract-The increasing interest in archiving all of humankind's cultural artifacts has resulted in the digitization of millions of books, and soon a significant fraction of the world's books will be online. Most of the data in historical manuscripts is text, but there is also a significant fraction devoted to images. This fact has driven much of the recent increase in interest in query-by-content systems for images. While querying/indexing systems can undoubtedly be useful, we believe that the historical manuscript domain is finally ripe for true unsupervised discovery of patterns and regularities. To this end, we introduce an efficient and scalable system which can detect approximately repeated occurrences of shape patterns both within and between historical texts. We show that this ability to find repeated shapes allows automatic annotation of manuscripts, and allows users to trace the evolution of ideas. We demonstrate our ideas on datasets of scientific and cultural manuscripts dating back to the fourteenth century.

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Section: Discussionmentioning

confidence: 96%

Mining Historical Documents for Near-Duplicate Figures

Rakthanmanon

Zhu

Keogh

2011

2011 IEEE 11th International Conference on Data Mining

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“…SAX has been used to asses different problems such as finding time series discords [24], finding motifs in a database of shapes [25], and to compress data before finding abnormal deviations [26] and it has repeatedly been enhanced [27], [28], [29].…”

Section: Symbolic Aggregate Approximation (Sax)mentioning

confidence: 99%

BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation

González-Vidal

Barnaghi

Skarmeta

2018

IEEE Trans. Knowl. Data Eng.

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Abstract-The massive collection of data via emerging technologies like the Internet of Things (IoT) requires finding optimal ways to reduce the observations in the time series analysis domain. The IoT time series require aggregation methods that can preserve and represent the key characteristics of the data. In this paper, we propose a segmentation algorithm that adapts to unannounced mutations of the data (i.e. data drifts). The algorithm splits the data streams into blocks and groups them in square matrices, computes the Discrete Cosine Transform (DCT) and quantizes them. The key information is contained in the upper-left part of the resulting matrix. We extract this sub-matrix, compute the modulus of its eigenvalues and remove duplicates. The algorithm, called BEATS, is designed to tackle dynamic IoT streams, whose distribution changes over time. We implement experiments with six datasets combining real, synthetic, real-world data, and data with drifts. Compared to other segmentation methods like Symbolic Aggregate approXimation (SAX), BEATS shows significant improvements. Trying it with classification and clustering algorithms it provides efficient results. BEATS is an effective mechanism to work with dynamic and multi-variate data, making it suitable for IoT data sources. The datasets, code of the algorithm and the analysis results can be accessed publicly at: https://github.com/auroragonzalez/BEATS.

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“…Keogh et al [16] in 2006 detailed the process of converting a 2-D shape into a time series and matching two shapes based on the motifs discovered in these time series, allowing for rotations. Yankow et al [36] and Xi et al [34] extend this framework further to more complex scenarios of matching and similarity search. In 2007, Yankow et al [37] presented a uniform scaling approach to match differently scaled shapes.…”

Section: Related Workmentioning

confidence: 99%

The Deshredder: A visual analytic approach to reconstructing shredded documents

Butler

Chakraborty

Ramakrishan

2012

2012 IEEE Conference on Visual Analytics Science and Technology (VAST)

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Reconstruction of shredded documents remains a significant challenge. Creating a better document reconstruction system enables not just recovery of information accidentally lost but also understanding our limitations against adversaries' attempts to gain access to information. Existing approaches to reconstructing shredded documents adopt either a predominantly manual (e.g., crowd-sourcing) or a near automatic approach. We describe Deshredder, a visual analytic approach that scales well and effectively incorporates user input to direct the reconstruction process. Deshredder represents shredded pieces as time series and uses nearest neighbor matching techniques that enable matching both the contours of shredded pieces as well as the content of shreds themselves. More importantly, Deshredder's interface support visual analytics through user interaction with similarity matrices as well as higher level assembly through more complex stitching functions. We identify a functional task taxonomy leading to design considerations for constructing deshredding solutions, and describe how Deshredder applies to problems from the DARPA Shredder Challenge through expert evaluations.

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Finding Motifs in a Database of Shapes

Cited by 35 publications

References 35 publications

Mining Historical Documents for Near-Duplicate Figures

Mining Historical Documents for Near-Duplicate Figures

BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation

The Deshredder: A visual analytic approach to reconstructing shredded documents

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