With the abundance of raw data generated from various sources, Big Data has become a preeminent approach in acquiring, processing, and analyzing large amounts of heterogeneous data to derive valuable evidences. The size, speed, and formats in which data is generated and processed affect the overall quality of information. Therefore, Quality of Big Data (QBD) has become an important factor to ensure that the quality of data is maintained at all Big data processing phases. This paper addresses the QBD at the pre-processing phase, which includes sub-processes like cleansing, integration, filtering, and normalization. We propose a QBD model incorporating processes to support Data quality profile selection and adaptation. In addition, it tracks and registers on a data provenance repository the effect of every data transformation happened in the pre-processing phase. We evaluate the data quality selection module using large EEG dataset. The obtained results illustrate the importance of addressing QBD at an early phase of Big Data processing lifecycle since it significantly save on costs and perform accurate data analysis.

With the advances in communication technologies and the high amount of data generated, collected, and stored, it becomes crucial to manage the quality of this data deluge in an efficient and cost-effective way. The storage, processing, privacy and analytics are the main keys challenging aspects of Big Data that require quality evaluation and monitoring. Quality has been recognized by the Big Data community as an essential facet of its maturity. Yet, it is a crucial practice that should be implemented at the earlier stages of its lifecycle and progressively applied across the other key processes. The earlier we incorporate quality the full benefit we can get from insights. In this paper, we first identify the key challenges that necessitates quality evaluation. We then survey, classify and discuss the most recent work on Big Data management. Consequently, we propose an across-the-board quality management framework describing the key quality evaluation practices to be conducted through the different Big Data stages. The framework can be used to leverage the quality management and to provide a roadmap for Data scientists to better understand quality practices and highlight the importance of managing the quality. We finally, conclude the paper and point to some future research directions on quality of Big Data.

Data is the most valuable asset companies are proud of. When its quality degrades, the consequences are unpredictable, can lead to complete wrong insights. In Big Data context, evaluating the data quality is challenging, must be done prior to any Big data analytics by providing some data quality confidence. Given the huge data size, its fast generation, it requires mechanisms, strategies to evaluate, assess data quality in a fast, efficient way. However, checking the Quality of Big Data is a very costly process if it is applied on the entire data. In this paper, we propose an efficient data quality evaluation scheme by applying sampling strategies on Big data sets. The Sampling will reduce the data size to a representative population samples for fast quality evaluation. The evaluation targeted some data quality dimensions like completeness, consistency. The experimentations have been conducted on Sleep disorder's data set by applying Big data bootstrap sampling techniques. The results showed that the mean quality score of samples is representative for the original data, illustrate the importance of sampling to reduce computing costs when Big data quality evaluation is concerned. We applied the Quality results generated as quality proposals on the original data to increase its quality.

Big Data has gained an enormous momentum the past few years because of the tremendous volume of generated and processed Data from diverse application domains. Nowadays, it is estimated that 80% of all the generated data is unstructured. Evaluating the quality of Big data has been identified to be essential to guarantee data quality dimensions including for example completeness, and accuracy. Current initiatives for unstructured data quality evaluation are still under investigations. In this paper, we propose a quality evaluation model to handle quality of Unstructured Big Data (UBD). The later captures and discover first key properties of unstructured big data and its characteristics, provides some comprehensive mechanisms to sample, profile the UBD dataset and extract features and characteristics from heterogeneous data types in different formats. A Data Quality repository manage relationships between Data quality dimensions, quality Metrics, features extraction methods, mining methodologies, data types and data domains. An analysis of the samples provides a data profile of UBD. This profile is extended to a quality profile that contains the quality mapping with selected features for quality assessment. We developed an UBD quality assessment model that handles all the processes from the UBD profiling exploration to the Quality report. The model provides an initial blueprint for quality estimation of unstructured Big data. It also, states a set of quality characteristics and indicators that can be used to outline an initial data quality schema of UBD.