用探照灯进行波形数据的交互式搜索和探索

A. Kalinin, U. Çetintemel, S. Zdonik
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引用次数: 3

摘要

Searchlight可以交互式地搜索和探索大型多维数据集。它允许用户通过为他们感兴趣的“对象”指定丰富的约束来进行探索。约束可以表达各种属性,包括对象的形状(例如,波形间隔长度为10-100ms),其聚合属性(例如,间隔内信号的平均幅度大于10),以及与另一个对象的相似性(例如,间隔的波形与查询波形之间的距离小于5)。Searchlight允许用户指定任意数量的此类约束,在同一查询中混合不同类型的约束。Searchlight增强了数组DBMS(目前为SciDB)的查询执行引擎,使其能够使用约束编程(CP)的功能执行复杂的搜索。这允许Or-Tools(来自b谷歌的开源操作研究工具套件)中的现有CP求解器直接访问DBMS中的数据,而无需提取和转换数据。本次演示将通过使用真实世界的MIMIC II数据集(包含ICU患者多参数记录的波形数据,如ABP(动脉血压)和ECG(心电图)),展示Searchlight丰富的搜索和探索功能及其创新的技术特征。用户将能够通过指定相应信号的聚合属性来搜索感兴趣的波形间隔。此外,他们将能够搜索与已经发现的相似的区间,其中相似性被定义为信号序列之间的距离。
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Interactive Search and Exploration of Waveform Data with Searchlight
Searchlight enables search and exploration of large, multi-dimensional data sets interactively. It allows users to explore by specifying rich constraints for the "objects" they are interested in identifying. Constraints can express a variety of properties, including a shape of the object (e.g., a waveform interval of length 10-100ms), its aggregate properties (e.g., the average amplitude of the signal over the interval is greater than 10), and similarity to another object (e.g., the distance between the interval's waveform and the query waveform is less than 5). Searchlight allows users to specify an arbitrary number of such constraints, with mixing different types of constraints in the same query. Searchlight enhances the query execution engine of an array DBMS (currently SciDB) with the ability to perform sophisticated search using the power of Constraint Programming (CP). This allows an existing CP solver from Or-Tools (an open-source suite of operations research tools from Google) to directly access data inside the DBMS without the need to extract and transform it. This demo will illustrate the rich search and exploration capabilities of Searchlight, and its innovative technical features, by using the real-world MIMIC II data set, which contains waveform data for multi-parameter recordings of ICU patients, such as ABP (Arterial Blood Pressure) and ECG (electrocardiogram). Users will be able to search for interesting waveform intervals by specifying aggregate properties of the corresponding signals. In addition, they will be able to search for intervals similar to already found, where similarity is defined as a distance between the signal sequences.
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