… Existing recovery approaches fall short of efficiency owing to enumerating and searching …
of events. In this paper, we study the efficient techniques for recovering missing events. …

… the possible sequences of events. In this paper, we study the efficient techniques for recovering
missing events. According to our theoretical results, the recovery problem appears to be …

… recover the missing events without any predefined knowledge. In this paper, we study the
problem of automatic recovery of missing events … is of great importance to efficiently recover the …

… all the possible sequences of events. In this paper, we study the efficient techniques for
recovering missing events. According to our theoretical results, the recovery problem is proved to …

… Therefore, in this case, data recovery approaches and sparse sampling [4] is one of the
energy-efficient methods to eliminate the data missing problem in sensor networks. The data …

… have employed matrix completion techniques to recover missing data by exploiting the low-…
Drawing on this insight, we propose a missing data recovery algorithm based on Temporal …

… missing data scenario via offline analytics. Specifically, for each missing data condition, we
store the most likely recovery … we do not need exact recovery of the missing data as long as …

… this method can achieve data identification and recovery efficiently solely based on data, and
… Additionally, most of the references do not investigate small probability events. Therefore, it …

… efficient algorithm for missing data reconstruction (imputation), that is specifically designed
to recover off-period segments of missing … immediately prior to the missing event, and (iii) the …

… a missing values recovering in a massive missing values context. It was compared the efficiency
of … the system in terms of occurrences of events. For example, it can be reason if a given …