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Continuous data cleaning
In declarative data cleaning, data semantics are encoded as constraints and errors arise
when the data violates the constraints. Various forms of statistical and logical inference can …
when the data violates the constraints. Various forms of statistical and logical inference can …
Privacy-aware data cleaning-as-a-service
Data cleaning is a pervasive problem for organizations as they try to reap value from their
data. Recent advances in networking and cloud computing technology have fueled a new …
data. Recent advances in networking and cloud computing technology have fueled a new …
PACAS: Privacy-aware, data cleaning-as-a-service
Data cleaning consumes up to 80% of the data analysis pipeline. This is a significant
overhead for organizations where data cleaning is still a manually driven process requiring …
overhead for organizations where data cleaning is still a manually driven process requiring …
Infoclean: Protecting sensitive information in data cleaning
Data quality has become a pervasive challenge for organizations as they wrangle with large,
heterogeneous datasets to extract value. Given the proliferation of sensitive and confidential …
heterogeneous datasets to extract value. Given the proliferation of sensitive and confidential …
[PDF][PDF] Keyminer: Discovering keys for graphs
Keys allow us to uniquely identify entities in a graph database. They have applications in
object identification, entity resolution, knowledge fusion, and social network reconciliation …
object identification, entity resolution, knowledge fusion, and social network reconciliation …
Contextual data cleaning
In this paper, we motivate the need to include context in data cleaning in order to account for
the subjective nature of data quality. Based on our recent work on incorporating ontologies …
the subjective nature of data quality. Based on our recent work on incorporating ontologies …
A provenance-based approach to manage long term preservation of scientific data
Long term preservation of scientific data goes beyond the data, and extends to metadata
preservation and curation. While several researchers emphasize curation processes, our …
preservation and curation. While several researchers emphasize curation processes, our …
Models for distributed, large scale data cleaning
Poor data quality is a serious and costly problem affecting organizations across all
industries. Real data is often dirty, containing missing, erroneous, incomplete, and duplicate …
industries. Real data is often dirty, containing missing, erroneous, incomplete, and duplicate …
Performance evaluation of Mobile IPv6 handover extensions in an IEEE 802.11 b wireless network environment
In order to support mobile users, the basic Internet protocols have been extended with
protocols (eg, Mobile IPv6) for intercepting and forwarding packets to a mobile and possibly …
protocols (eg, Mobile IPv6) for intercepting and forwarding packets to a mobile and possibly …
A data quality framework for customer relationship analytics
Poor data quality has become an increasingly pervasive problem for organizations leading
to operational inefficiency, increased costs, and missed opportunities. As high quality data is …
to operational inefficiency, increased costs, and missed opportunities. As high quality data is …