The concept of a mobile database brings some characteristics from distributed systems and incorporates the growing developments in wireless technology and mobile devices. At a practical level, a mobile database solution allows a business user to have connectivity to a corporate central database while in the field, without a dedicated link to the corporate database server. The method of communication is not unlike asynchronous replication, wherein the updates between mobile node and corporate node are handled on a schedule as opposed to instantaneously. According to Connolly & Begg (2015), mobile database solutions typically include:

1. a corporate database server and DBMS that manages and stores the corporate data and provides corporate applications;
2. a remote database and DBMS that manages and stores the mobile data and provides mobile applications;
3. a mobile database platform that includes laptop, smartphone, or other Internet access devices;
4. two-way communication links between the corporate and mobile DBMS.

Beyond the functions of a standard DBMS, mobile database solutions also require the ability to:

1. communicate with the centralized database server through modes such as wireless or Internet access;
2. replicate data on the centralized database server and mobile device;
3. synchronize data on the centralized database server and mobile device;
4. capture data from various sources such as the Internet;
5. manage data on the mobile device;
6. analyze data on a mobile device;
7.  create customized mobile applications (Connolly & Begg, 2015).

Common issues in mobile database solutions include security, network partitioning, cellular handoff, and ACID transaction management (Connolly & Begg, 2015; Ibikunle & Adegbenjo, 2013). Security, partitioning, and handoff are all inherent in the mobile nature of the solution; that is, the idea of mobile nodes roaming around the country, with the connections being handed off between cellular towers as the user traverses a route, obviously carries with it the possibilities of signal loss or physical loss of an unsecured device.

ACID principles, which address the cluster as a whole entity, must be relaxed and adapted for mobile database solutions (Connolly & Begg, 2015). Assume for a moment that a large number of modifications are done by a user on the mobile node. Those transactions must be committed to the central database at next update, but if the connection is lost or weakened as a cellular signal is handed off, the batch may not complete. In that case, according to strict Atomicity rules, the entire set of transactions must be rolled back. This is not optimal and thus a new approach to Atomicity must be defined for mobile solutions. During that time, isolation is an issue, because a resource is blocked until the transaction is released. This also brings in the questions of consistency and durability: what happens when connectivity is lost and the mobile database is inconsistent with the central database? It is not discoverable until the mobile database is able to re-establish connectivity. What happens if one or more components of the mobile database solution experiences a failure?

Similarly, as CAP concerns are raised when a network partition occurs, we must take into account additional partition likelihoods of mobile handoff or signal loss. The mobile database solution must choose between consistency or availability in an event of partition. In choosing consistency, none of the nodes will be available until they are all back online. In choosing availability, the nodes will be available but not necessarily consistent until connectivity is re-established between all nodes.

The shortcomings in both ACID and CAP are mostly relegated to CAP, which applies to the database solution as a whole. The system overall must be available. However, consistency is possible in a slightly more relaxed way (just as ACID properties tend to be more relaxed for mobile database solutions). Ramakrishnan (2012) acknowledges that consistency can exist on a spectrum.

References

Connolly, T., & Begg, C. (2015).  Database Systems – Database Systems: A Practical Approach to Design, Implementation, and Management (6th ed.). London, UK: Pearson.

Frank, L., Pedersen, R. U., Frank, C. H., & Larsson, N. J. (2014). The cap theorem versus databases with relaxed acid properties. Paper presented at the Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication, Siem Reap, Cambodia.

Greiner, R. (2014). Cap theorem: Revisited.  Retrieved from http://robertgreiner.com/2014/08/cap-theorem-revisited/

Ibikunle, F. A., & Adegbenjo, A. A. (2013). Management issues and challenges in mobile database system. International Journal of Engineering Sciences & Emerging Technologies, 5(1).

Shapiro, M., Preguiça, N., Baquero, C., & Zawirski, M. (2011). Conflict-free replicated data types. Paper presented at the Stabilization, Safety, and Security of Distributed Systems, Berlin, Heidelberg.

Ulnes, S. A. (2017). Eventually consistent: A mobile-first distributed system.  Retrieved from https://academy.realm.io/posts/eventually-consistent-making-a-mobile-first-distributed-system/