Data Inconsistency Evaluation for Cyberphysical System

Abstract

Cyberphysical systems (CPSs) have been widely applied in a variety of applications to collect data, while data is often dirty in reality. We pay attention to the way of evaluating data inconsistency which is a major concern for evaluating quality of data and its source. This paper is the first study on data inconsistency evaluation problem for CPS based on conditional functional dependencies. Given a database instance including tuples and a CFD set Σ including CFDs, data inconsistency is defined as the ratio of the size of minimum culprit in , where a culprit is a set of tuples leading to integrity errors. Firstly, we give a sufficient analysis on the complexity and inapproximability of minimum culprit problem. Then, we provide a practical algorithm that gives a 2-approximation of the data dirtiness in ( log ) time based on independent residual subgraph. To deal with the large dynamic data, we provide a compact structure based on B-tree for storing independent residual subgraph in order to update inconsistency efficiently. At last, we test our algorithm on both synthetic and real-life datasets; the experiment results show the scalability of our algorithm and the quality of the evaluation result.

Assessing data quality – A probability-based metric for semantic consistency

“…Finally, the metrics provided in the last two rows of Table 1 with the dark grey background (Abboura et al, 2016;Wang et al, 2016) assess consistency by using CFDs. A CFD is a pair ( → , ) consisting of a functional dependency → (an implication of sets of attributes) and a certain tableau (with ∈ {1, 2, … , }) which specifies values for the attributes in and (cf.…”

“…(2)). Wang et al (2016) propose to determine a minimum subset of tuples in a database which -if corrected -would lead to the database fulfilling all CFDs. Then, the inconsistency of the database is measured by the ratio of the size of this minimum subset in relation to the size of the whole database.…”