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Many applications require storage and indexing of new kinds of data in main-memory, e.g. color histograms, textures, shape features, gene sequences, sensor readings, or financial time series. Even though, many domain index structures were developed, very a few of them are implemented in any database management system (DBMS), usually only B-trees and hash indexes. A major reason is that the manual effort to include a new index implementation in a regular DBMS is very costly and time-consuming because it requires integration with all components of the DBMS kernel. To alleviate this, there are some extensible indexing frameworks. However, they all require re-engineering the index implementations, which is a problem when the index has third-party ownership, when only binary code is available, or simply when the index implementation is complex to re-engineer. Therefore, the DBMS should allow including new index implementations without code changes and performance degradation. Furthermore, for high performance the query processor needs knowledge of how to process queries to utilize plugged-in index. Moreover, it is important that all functionalities of a plugged-in index implementation are correct.

The extensible main memory database system (MMDB) Mexima (Main-memory External Index Manager) addresses these challenges. It enables transparent plugging in main-memory index implementations without code changes. Index specific rewrite rules transform complex queries to utilize the indexes. Automatic test procedures validate the correctness of them based on user provided index meta-data. Moreover, the same optimization framework can also optimize complex queries sent to a back-end DBMS by exposing hidden indexes for its query optimizer.

Altogether, Mexima is a complete and extensible platform for transparently index integration, utilization, and evaluation.

Internet of Things (IoT) often involve considerable numbers of sensors that produce large volumes of data. In this context, efficient management of data could potentially enable automatic decision making based on analytics of sensors on equipment. However, these sensors are often geographically distributed and generate diverse formats of data in form of sensor streams at a high rate. The combination of these properties of IoT pose significant challenges for the existing database management systems (DBMSs) to provide scalable data storage and analytics.

The problem of providing efficient data management of distributed IoT applications using DBMS technologies is addressed in this thesis. Initially, we developed a prototype system, Fused LOg database Query Processor (FLOQ), which enables general query processingover collections of relational databases that are deployed locally on distributed sites to store sensor measurement logs. Although FLOQ provides efficient query execution when scaling the number of distributed databases, it exhibits complexity and scalability issues for large IoT applications having heterogeneous data. The limitations of FLOQ are primarily inherent to its use of relational database backends for storage of sensor logs.

When a relational database is used to store large-scale IoT data, it exhibits several challenges. The loading of massive logs produced at high rates is not fast enough due to its strong consistency mechanisms. Furthermore, it could demonstrate a single point of failure that limits the availability, and the inflexible schemas make it difficult to manage heterogeneity. In contrast to relational databases, distributed NoSQL data stores could provide scalable storage of heterogeneous data through data partitioning, replication, and high availability by sacrificing strong consistency. To understand the suitability of NoSQL databases, this thesis also investigates to what degree NoSQL DBMSs provide scalable storage and analytics of IoT applications by comparing a variety of state-of-the-art relational and NoSQL databases for real-world industrial IoT data. 

The experimental evaluations reveal that the scalability can be provided by the distributed NoSQL data stores; however, the support of advanced data analytics is difficult due to their limited query processing capabilities. Furthermore, data management of distributed IoT applications often requires seamless integration between a real-time edge analytics platform, a distributed storage manager, effective data integration, and query processing techniques for handling heterogeneity. Therefore, in order to provide a holistic data management solution, this thesis developed the Extended Query Processing (EQP) system, which enables advanced analytics for supporting both edge and offline analytics for large-scale IoT applications.

These contributions enable efficient data management of large-scale heterogeneous IoT applications and supports advanced analytics.