데이터셋 탐색

This dataset contains expert-labeled telemetry anomaly data from the Mars Science Laboratory (MSL) rover, Curiosity. Real spacecraft and curiosity rover anomalies for anomaly detection Indications of telemetry anomalies can be found within previously mentioned ISA reports. All telemetry channels discussed in an individual ISA were reviewed to ensure that the anomaly was evident in the associated telemetry data, and specific anomalous time ranges were manually labeled for each channel. If multiple anomalous sequences and channels closely resembled each other, only one was kept for the experiment in order to create a diverse and balanced set. Anomalies were classified into two categories, point and contextual, to distinguish between anomalies that would likely be identified by properly set alarms or distance-based methods that ignore temporal information (point anomalies) and those that require more complex methodologies such as LSTMs or Hierarchical Temporal Memory (HTM) approaches to detect (contextual anomalies) MSL: TM Channels (27) Total TM values (66,709) Total anomalies (36)

The largest and most diverse dataset for lifelong place recognition from image sequences in urban and suburban settings.

The MSLR-WEB10K dataset consists of 10,000 search queries over the documents from search results. The data also contains the values of 136 features and a corresponding user-labeled relevance factor on a scale of one to five with respect to each query-document pair. It is a subset of the MSLR-WEB30K dataset. Source: Dueling Bandits with Qualitative Feedback

The MSLR-WEB30K dataset consists of 30,000 search queries over the documents from search results. The data also contains the values of 136 features and a corresponding user-labeled relevance factor on a scale of one to five with respect to each query-document pair.

The datasets are machine learning data, in which queries and urls are represented by IDs. The datasets consist of feature vectors extracted from query-url pairs along with relevance judgment labels: (1) The relevance judgments are obtained from a retired labeling set of a commercial web search engine (Microsoft Bing), which take 5 values from 0 (irrelevant) to 4 (perfectly relevant). (2) The features are basically extracted by us, and are those widely used in the research community. In the data files, each row corresponds to a query-url pair. The first column is relevance label of the pair, the second column is query id, and the following columns are features. The larger value the relevance label has, the more relevant the query-url pair is. A query-url pair is represented by a 136-dimensional feature vector.

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