·         Data representation: many datasets have certain levels of heterogeneity in type, structure, semantics, organization, granularity, and accessibility. Data representation aims to make data more meaningful for computer analysis and user interpretation. Nevertheless, an improper data representation will reduce the value of the original data and may even obstruct effective data analysis. Efficient data representation shall reflect data structure, class, and type, as well as integrated technologies, so as to enable efficient operations on different datasets.

·         Redundancy reduction and data compression: generally, there is a high level of redundancy in datasets. Redundancy reduction and data compression is effective to reduce the indirect cost of the entire system on the premise that the potential values of the data are not affected. For example, most data generated by sensor networks are highly redundant, which may be filtered and compressed at orders of magnitude.

·         Data life cycle management: compared with the relatively slow advances of storage systems, pervasive sensing and computing are generating data at unprecedented rates and scales. We are confronted with a lot of pressing challenges, one of which is that the current storage system could not support such massive data. Generally speaking, values hidden in big data depend on data freshness. Therefore, a data importance principle related to the analytical value should be developed to decide which data shall be stored and which data shall be discarded.

·         Analytical mechanism: the analytical system of big data shall process masses of heterogeneous data within a limited time. However, traditional RDBMSs are strictly designed with a lack of scalability and expandability, which could not meet the performance requirements. Non-relational databases have shown their unique advantages in the processing of unstructured data and started to become mainstream in big data analysis. Even so, there are still some problems of non-relational databases in their performance and particular applications. We shall find a compromising solution between RDBMSs and non-relational databases. For example, some enterprises have utilized a mixed database architecture that integrates the advantages of both types of database (e.g., Facebook and Taobao). More research is needed on the in-memory database and sample data based on approximate analysis.

·         Data confidentiality: most big data service providers or owners at present could not effectively maintain and analyze such huge datasets because of their limited capacity. They must rely on professionals or tools to analyze such data, which increase the potential safety risks. For example, the transactional dataset generally includes a set of complete operating data to drive key business processes. Such data contains details of the lowest granularity and some sensitive information such as credit card numbers. Therefore, analysis of big data may be delivered to a third party for processing only when proper preventive measures are taken to protect such sensitive data, to ensure its safety.

·         Energy management: the energy consumption of mainframe computing systems has drawn much attention from both economy and environment perspectives. With the increase of data volume and analytical demands, the processing, storage, and transmission of big data will inevitably consume more and more electric energy. Therefore, system-level power consumption control and management mechanism shall be established for big data while the expandability and accessibility are ensured.

·         Expendability and scalability: the analytical system of big data must support present and future datasets. The analytical algorithm must be able to process increasingly expanding and more complex datasets.

·         Cooperation: analysis of big data is an interdisciplinary research, which requires experts in different fields cooperate to harvest the potential of big data. A comprehensive big data network architecture must be established to help scientists and engineers in various fields access different kinds of data and fully utilize their expertise, so as to cooperate to complete the analytical objectives.