Incomplete data management: a survey

Miao, Xiaoye; Gao, Yunjun; Guo, Su; Liu, Wanqi

doi:10.1007/s11704-016-6195-x

Cited by 33 publications

(15 citation statements)

References 70 publications

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“…This task is relevant for a number of other tasks beyond table completion, including entity search [18] and knowledge base completion [39]. The completion of rows is similar to the broad problem of imputation and dealing with incomplete data [99]. Specific work in the context of the web has looked at performing imputation through the use of external data [6,92,126].…”

Section: Tabular Searchmentioning

confidence: 99%

Dataset search: a survey

et al. 2019

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Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts to data marketplaces, open data portals and data communities. Google recently beta released a search service for datasets, which allows users to discover data stored in various online repositories via keyword queries. These developments foreshadow an emerging research field around dataset search or retrieval that broadly encompasses frameworks, methods and tools that help match a user data need against a collection of datasets. Here, we survey the state of the art of research and commercial systems in dataset retrieval. We identify what makes dataset search a research field in its own right, with unique challenges and methods and highlight open problems. We look at approaches and implementations from related areas dataset search is drawing upon, including information retrieval, databases, entity-centric and tabular search in order to identify possible paths to resolve these open problems as well as immediate next steps that will take the field forward.

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Section: Tabular Searchmentioning

confidence: 99%

Dataset search: a survey

et al. 2019

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“…Then, we will find appropriate location in ST to insert object o p i , either on the first layer or on another layer pointed by a parent node, parentN ode (lines 4-29). Finally, we will update the ST index by removing those objects dominated by o p i and finding children of object o p i in ST (lines [30][31][32][33][34][35][36][37][38][39][40]. Finding the location to insert new object o p i .…”

Section: Dynamic Maintenance Of the Skyline Treementioning

confidence: 99%

Skyline queries over incomplete data streams

2019

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Nowadays, efficient and effective processing over massive stream data has attracted much attention from the database community, which are useful in many real applications such as sensor data monitoring, network intrusion detection, and so on. In practice, due to the malfunction of sensing devices or imperfect data collection techniques, real-world stream data may often contain missing or incomplete data attributes. In this paper, we will formalize and tackle a novel and important problem, named skyline query over incomplete data stream (Sky-iDS), which retrieves skyline objects (in the presence of missing attributes) with high confidences from incomplete data stream. In order to tackle the Sky-iDS problem, we will design efficient approaches to impute missing attributes of objects from incomplete data stream via differential dependency (DD) rules. We will propose effective pruning strategies to reduce the search space of the Sky-iDS problem, devise cost-model-based index structures to facilitate the data imputation and skyline computation at the same time, and integrate our proposed techniques into an efficient Sky-iDS query answering algorithm. Extensive experiments have been conducted to confirm the efficiency and effectiveness of our Sky-iDS processing approach over both real and synthetic data sets.

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“…Crowdsourcing is widely used to extract entities and knowledge from massive types of data [5,6,14,27,34]. Chai et al [4] focused on collecting entities using crowdsourcing with low cost and high quality.…”

Section: Entity Extraction and Knowledge Graph Construction With Crowmentioning

confidence: 99%

Towards Automatic Mathematical Exercise Solving

et al. 2019

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Knowledge graphs are widely applied in many applications. Automatically solving mathematical exercises is also an interesting task which can be enhanced by knowledge reasoning. In this paper, we design MathGraph, a knowledge graph aiming to solve high school mathematical exercises. Since it requires fine-grained mathematical derivation and calculation of different mathematical objects, we design a crowdsourcing-based method to help build MathGraph. MathGraph supports massive kinds of mathematical objects, operations and constraints which may be involved in exercises. Furthermore, we propose an algorithm to align a semantically parsed exercise to MathGraph and figure out the answer automatically. Extensive experiments on real-world datasets verify the effectiveness of MathGraph.

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Incomplete data management: a survey

Cited by 33 publications

References 70 publications

Dataset search: a survey

Dataset search: a survey

Skyline queries over incomplete data streams

Towards Automatic Mathematical Exercise Solving

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