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- 01HMTX2Y65WBNMYFQTF94XX2Q0 classification P1.
- 01HMTX2Y65WBNMYFQTF94XX2Q0 date "2023".
- 01HMTX2Y65WBNMYFQTF94XX2Q0 language "eng".
- 01HMTX2Y65WBNMYFQTF94XX2Q0 type conference.
- 01HMTX2Y65WBNMYFQTF94XX2Q0 hasPart 01HMTX5T1Y0RYRRTJC41TEJ94G.pdf.
- 01HMTX2Y65WBNMYFQTF94XX2Q0 hasPart 01HMTX67Q81VF2C95V8CK1G5X4.pdf.
- 01HMTX2Y65WBNMYFQTF94XX2Q0 subject "Technology and Engineering".
- 01HMTX2Y65WBNMYFQTF94XX2Q0 doi "10.1109/vis54172.2023.00013".
- 01HMTX2Y65WBNMYFQTF94XX2Q0 isbn "9798350325577".
- 01HMTX2Y65WBNMYFQTF94XX2Q0 issn "2771-9537".
- 01HMTX2Y65WBNMYFQTF94XX2Q0 issn "2771-9553".
- 01HMTX2Y65WBNMYFQTF94XX2Q0 presentedAt urn:uuid:85d974f2-b55d-41dd-8a16-c99a2bb6ee01.
- 01HMTX2Y65WBNMYFQTF94XX2Q0 abstract "Visualization plays an important role in the analysis and exploration of time series data. To facilitate efficient visualization of large datasets, downsampling has emerged as a well-established approach. This work concentrates on LTTB (Largest-Triangle-Three-Buckets), a widely adopted downsampling algorithm for time series data point selection. Specifically, we introduce MinMaxLTTB, a two-step algorithm that significantly improves the scalability of LTTB. MinMaxLTTB consists of the following two steps: (i) the MinMax algorithm preselects a certain ratio of minimum and maximum data points, followed by (ii) applying the LTTB algorithm on only these preselected data points, effectively reducing LTTB’s time complexity. The MinMax algorithm is computationally efficient and can be parallelized, enabling efficient data point preselection. Additionally, MinMax demonstrates competitive performance in terms of visual representation, making it also an effective data reduction method. Experimental results demonstrate that MinMaxLTTB outperforms LTTB by more than an order of magnitude in terms of computation time. Furthermore, preselecting a small multiple of the desired output size already yields similar visual representativeness compared to LTTB. In summary, MinMaxLTTB leverages the computational efficiency of MinMax to scale LTTB, without compromising on LTTB its favorable visualization properties. The code and experiments associated with this paper can be found at https://github.com/predict-idlab/MinMaxLTTB.".
- 01HMTX2Y65WBNMYFQTF94XX2Q0 author 0a4ff2ca-e97a-11ea-941f-d0399d38aec8.
- 01HMTX2Y65WBNMYFQTF94XX2Q0 author DBD056C2-45F4-11E5-91E6-AC9EB5D1D7B1.
- 01HMTX2Y65WBNMYFQTF94XX2Q0 author F6BA63F6-F0ED-11E1-A9DE-61C894A0A6B4.
- 01HMTX2Y65WBNMYFQTF94XX2Q0 author F721863A-F0ED-11E1-A9DE-61C894A0A6B4.
- 01HMTX2Y65WBNMYFQTF94XX2Q0 dateCreated "2024-01-23T10:15:36Z".
- 01HMTX2Y65WBNMYFQTF94XX2Q0 dateModified "2024-11-27T23:16:24Z".
- 01HMTX2Y65WBNMYFQTF94XX2Q0 name "MinMaxLTTB : leveraging MinMax-preselection to scale LTTB".
- 01HMTX2Y65WBNMYFQTF94XX2Q0 pagination urn:uuid:b73a28ad-7cb7-4b67-adfc-64602ce198bb.
- 01HMTX2Y65WBNMYFQTF94XX2Q0 publisher urn:uuid:039c86ec-7049-46e6-b27a-208ade5c95f9.
- 01HMTX2Y65WBNMYFQTF94XX2Q0 sameAs LU-01HMTX2Y65WBNMYFQTF94XX2Q0.
- 01HMTX2Y65WBNMYFQTF94XX2Q0 sourceOrganization urn:uuid:68aa4827-9d62-4ac7-b612-ca953a8e6c3f.
- 01HMTX2Y65WBNMYFQTF94XX2Q0 sourceOrganization urn:uuid:b95a080c-1309-4c90-b452-23ac28c0231d.
- 01HMTX2Y65WBNMYFQTF94XX2Q0 sourceOrganization urn:uuid:dfaa5a5b-8c4e-4af6-92ee-94bd0632c217.
- 01HMTX2Y65WBNMYFQTF94XX2Q0 type P1.