Combining user interaction, speculative query execution and sampling in the
            <i>DICE</i>
            system

Jayachandran, Prasanth; Tunga, Karthik; Kamat, Niranjan; Nandi, Arnab

doi:10.14778/2733004.2733064

Cited by 27 publications

(19 citation statements)

References 24 publications

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“…There has been a growing interest in interactive query processing and data exploration, which resulted in a number of systems. Some systems, such as Tableau [4] or Dice [7], allow users to perform navigation-like data exploration with interactive speeds. Such exploration usually involves queries similar to SQL group-bys, joins, ranges, etc.…”

Section: Discussionmentioning

confidence: 99%

Interactive Search and Exploration of Waveform Data with Searchlight

Kalinin

Çetintemel

Zdonik

2016

Proceedings of the 2016 International Conference on Management of Data

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Searchlight [9] enables search and exploration of large, multidimensional 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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Section: Discussionmentioning

confidence: 99%

Interactive Search and Exploration of Waveform Data with Searchlight

Kalinin

Çetintemel

Zdonik

2016

Proceedings of the 2016 International Conference on Management of Data

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“…This allows for efficient access and updates of sampled data sets. More recently, DICE combines sampling with speculative execution in the same engine [35]. Approximation and sampling has also been coined as a key approach to support interactive visual analytics at the core of a DB engine [32].…”

Section: Database Layermentioning

confidence: 99%

Overview of Data Exploration Techniques

Idreos

Papaemmanouil

Chaudhuri

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

201

103

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Data exploration is about efficiently extracting knowledge from data even if we do not know exactly what we are looking for. In this tutorial, we survey recent developments in the emerging area of database systems tailored for data exploration. We discuss new ideas on how to store and access data as well as new ideas on how to interact with a data system to enable users and applications to quickly figure out which data parts are of interest. In addition, we discuss how to exploit lessons-learned from past research, the new challenges data exploration crafts, emerging applications and future research directions.

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“…Materialization of secondary structures and prediction approaches such as DICE [46], are good examples in this field.…”

Section: Future Plansmentioning

confidence: 99%

On Improving User Response Times in Tableau

Terlecki

Shaw

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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The rapid increase in data volumes and complexity of applied analytical tasks poses a big challenge for visualization solutions. It is important to keep the experience highly interactive, so that users stay engaged and can perform insightful data exploration.Query processing usually dominates the cost of visualization generation. Therefore, in order to achieve acceptable response times, one needs to utilize backend capabilities to the fullest and apply techniques, such as caching or prefetching. In this paper we discuss key data processing components in Tableau: the query processor, query caches, Tableau Data Engine [1, 2] and Data Server. Furthermore, we cover recent performance improvements related to the number and quality of remote queries, broader reuse of cached data, and application of inter and intra query parallelism.

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Combining user interaction, speculative query execution and sampling in the DICE system

Cited by 27 publications

References 24 publications

Interactive Search and Exploration of Waveform Data with Searchlight

Interactive Search and Exploration of Waveform Data with Searchlight

Overview of Data Exploration Techniques

On Improving User Response Times in Tableau

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