The deployment of machine learning models into production environments is a crucial task. Its seamless integration with the operational system can be quite challenging as it must adhere to the same software requirements such as memory management, latency, and scalability as the rest of the system. Unfortunately, none of these requirements are taken into consideration when inferring new models from data. A straightforward approach for deployment consists of building a pipeline connecting the modeling tools to the operational system. This approach follows a client-server architecture and it may not address the design requirements of the software in production, especially the ones related to efficiency. An alternative is to manually generate the source code implementing the model in the programming language that was originally used to develop the software in production. However, this approach is usually avoided because it is a time-consuming and error-prone task. To circumvent the aforementioned problems, we propose to automate the process of machine learning model deployment. For this, we have developed a special-purpose compiler. Machine learning models can be formally defined using a standard language. We use this formal description as an input for our compiler, which translates it into the source code that implements the model. Our proposed compiler generates code for different programming languages. Furthermore, with this compiler we can generate source code that exploits specific characteristics of the systems hardware architecture such as multi-core CPUs and graphic processing cards. We have conducted experiments that indicate that automated code generation for deploying machine learning models is, not only feasible but also efficient.

Spatiotemporal databases are becoming increasingly more common. Typically, applications modeling spatiotemporal objects need to process vast amounts of data. In such cases, generating aggregate information from the data set is more useful than individually analyzing every entry. In this paper, we study the most relevant techniques for the evaluation of aggregate queries on spatial, temporal, and spatiotemporal data. We also present a model that reduces the evaluation of aggregate queries to the problem of selecting qualifying tuples and the grouping of these tuples into collections on which an aggregate function is to be applied. This model gives us a framework that allows us to analyze and compare the different existing techniques for the evaluation of aggregate queries. At the same time, it allows us to identify opportunities for research on types of aggregate queries that have not been studied.

We have developed a new indexing strategy that helps overcome the curse of dimensionality for time series data. Our proposed approach, called skyline index, adopts new skyline bounding regions (SBR) to approximate and represent a group of time series data according to their collective shape. Skyline bounding regions allow us to define a distance function that tightly lower bounds the distance between a query and a group of time series data. In an extensive performance study, we investigate the impact of different distance functions by various dimensionality reduction and indexing techniques on the performance of similarity search, including index pages accessed, data objects fetched, and overall query processing time. In addition, we show that, for k-nearest neighbor queries, the proposed skyline index approach can be coupled with the state of the art dimensionality reduction techniques such as adaptive piecewise constant approximation (APCA) and improve its performance by up to a factor of 3.

The ability to model time-varying natures is essential to many database applications such as data warehousing and mining. However, the temporal aspects provide many unique characteristics and challenges for query processing and optimization. Among the challenges is computing temporal aggregates, which is complicated by having to compute temporal grouping. We introduce a variety of temporal aggregation algorithms that overcome major drawbacks of previous work. First, for small-scale aggregations, both the worst-case and average-case processing time have been improved significantly. Second, for large-scale aggregations, the proposed algorithms can deal with a database that is substantially larger than the size of available memory. Third, the parallel algorithm designed on a shared-nothing architecture achieves scalable performance by delivering nearly linear scale-up and speed-up, even at the presence of data skew. The contributions made in this paper are particularly important because the rate of increase in database size and response time requirements has out-paced advancements in processor and mass storage technology.

The ability to model time-varying nature is essential to many database applications such as data warehousing and mining. However, the temporal aspects provide many unique characteristics and challenges for query processing and optimization. Among the challenges is computing temporal aggregates, which is complicated by having to compute temporal grouping. In this paper, we introduce a variety of temporal aggregation algorithms that overcome major drawbacks of previous work. First, for small-scale aggregations, both the worst-case and average-case processing time have been improved significantly. Second, for large-scale aggregations, the proposed algorithms can deal with a database that is substantially larger than the size of available memory.