Automatic user steering for interactive data exploration

Dimitriadou, Kyriaki

doi:10.1109/icdew.2014.6818348

Cited by 31 publications

(69 citation statements)

References 18 publications

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“…Thus, the system is broken into two steps: first it learns the information need of the user by soliciting labels on certain examples from the user and then once the learning has completed, it suggests a query that may express the user's information need. These systems usually leverage active learning methods to learn the user intent by showing the fewest possible examples to the user [19]. However, ideally one would like to have a query interface in which the DBMS learns about the user's intents while answering her (vague) queries as our system does.…”

Section: Related Workmentioning

confidence: 99%

“…Query learning: Database community has proposed several systems that help the DBMS learn the user's information need by showing examples to the user and collecting her feedback [2,8,19,41,61]. In these systems, a user explicitly teaches the system by labeling a set of examples potentially in several steps without getting any answer to her information need.…”

Section: Related Workmentioning

confidence: 99%

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A Signaling Game Approach to Databases Querying and Interaction

Termehchy

Touri

2015

Proceedings of the 2015 International Conference on the Theory of Information Retrieval

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As most users do not precisely know the structure and/or the content of databases, their queries do not exactly reflect their information needs. The database management systems (DBMS) may interact with users and use their feedback on the returned results to learn the information needs behind their queries. Current query interfaces assume that users do not learn and modify the way way they express their information needs in form of queries during their interaction with the DBMS. Using a real-world interaction workload, we show that users learn and modify how to express their information needs during their interactions with the DBMS and their learning is accurately modeled by a well-known reinforcement learning mechanism. As current data interaction systems assume that users do not modify their strategies, they cannot discover the information needs behind users' queries effectively. We model the interaction between users and DBMS as a game with identical interest between two rational agents whose goal is to establish a common language for representing information needs in form of queries. We propose a reinforcement learning method that learns and answers the information needs behind queries and adapts to the changes in users' strategies and prove that it improves the effectiveness of answering queries stochastically speaking. We propose two efficient implementation of this method over large relational databases. Our extensive empirical studies over real-world query workloads indicate that our algorithms are efficient and effective.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Signaling Game Approach to Databases Querying and Interaction

Termehchy

Touri

2015

Proceedings of the 2015 International Conference on the Theory of Information Retrieval

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“…This leads to an exploratory session, in which a user executes numerous selection queries iteratively using different predicates [28]. Since, different queries within an exploratory session typically explore the data space in a close vicinity to each other, it is very likely for queries within a session to have overlapping results as shown in Figure 5.1.…”

Section: Sequential Diversificationmentioning

confidence: 99%

“…In literature, many techniques are also proposed for query recommendation [38]. Beyond query formulation, in [28] a new exploration interface is proposed that retrieves the relevant objects using only relevance feedback from users.…”

Section: Data Explorationmentioning

confidence: 99%

“…Hence, automated data exploration solutions have emerged to enable users to extract knowledge out of data with ease and efficiency (e.g., [15,28,50,52,69]). Here, we categorize the research efforts towards efficient data exploration along three phases of data exploration cycle namely: i) Query formulation/refinement, ii) Query execution, and iii) Result review.…”

Section: Data Explorationmentioning

confidence: 99%

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Scalable diversification for data exploration platforms

Khan¹

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Today, data exploration platforms are widely used to assist users in locating interesting objects within large volumes of scientific and business data. In those platforms, users try to make sense of the underlying data space by iteratively posing numerous queries over large databases.Hence, data exploration platforms rely on methods for the extraction of representative data to provide users with a concise and meaningful representation of query results. That is, extracting a few tuples from a query result to provide quick insights in the potentially huge answer space.In the past few years, importance of diversification while extracting representative subsets of data has been greatly emphasized. It has been shown that diverse subsets provide more effective representation of the underlying data by minimizing redundancy and increasing coverage.Meanwhile, search results diversification adds additional cost to an already computationally expensive exploration process.In this PhD thesis, we have focused on the design, implementation and evaluation of scalable diversification algorithms and schemes for the data exploration platforms. Particularly, this research stipulates that extracting diverse representative results during data exploration requires addressing several challenges including: 1) scaling to big volumes of high dimensional data, 2) large number of users, and 3) enabling real time continuous exploration. To address those challenges we focus on two broad aspects: 1) Diversification of high dimensional large data sets and 2) Diversification of multiple user queries.The existing work conducts diversification in two steps: first compute all relevant query results, and then diversify the query results to select a small diverse subset. Similarly, all the dimensional attributes of all the data points in a query result are considered for diversification. Such a generic approach would be a performance bottleneck in high-dimensional large databases. To efficiently compute diverse subsets of query results exhibiting both high dimensionality and high-cardinality, we have proposed the Progressive Diversification Scheme. Our proposed scheme, utilizes the partial distance computations to reduce the amount of CPU and I/O incurred during query diversification. Moreover, to avoid the overhead of computing all relevant results first, we propose embedding diversification in query evaluation step by utilizing column-based data storage systems. In addition to computational cost of diversification methods, we have also considered the complexity of diversity objective function in high dimensional databases. Often, computing diverse solutions along all the dimensions is not a realistic approach. In fact, users may have some pre-specified preferences over some dimensions of the data, while expecting good coverage over the other dimensions. Motivated by that need, we propose a novel scheme, which aims to generate representative data that balance the tradeoff iii between regret minimization and diversity maximization. Our scheme...

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