Large-scale critical infrastructures such as transportation, energy, or water distribution networks are increasingly equipped with smart sensor technologies. Low-latency analytics on the resulting times series would open the door to many exciting opportunities to improve our grasp on complex urban systems. However, sensor-generated time series often turn out to be noisy, non-uniformly sampled, and misaligned in practice, making them ill-suited for traditional data processing. In this paper, we introduce TRISTAN (massive TRIckletS Time series ANalysis), a new data management system for efficient storage and real-time processing of fine-grained time series data. TRISTAN relies on a dedicated, compressed sparse representation of the time series using a dictionary. In contrast to previous approaches, TRISTAN is able to execute most analytics queries on the compressed data directly, and supports efficient and approximate query answering based on the most significant atoms of the dictionary only. We present the overall architecture of our system and discuss its performance on several smarter city datasets, showing that TRISTAN can achieve up to 20:1 compression ratios and 250x speedup compared to a state-of-the-art system.

Smart sensors are increasingly being used to manage and monitor critical urban infrastructures, e.g., for telecommunication, transport, water, or energy networks, as well as for healthcare or smart buildings. Sensor-based monitoring systems offer ways of continuously monitoring low frequency activities, and open the door to new analytic and predictive applications in Smarter Cities. Such sensors generate “tricklets”, i.e., noisy and continuous time series. Tricklets are typically misaligned, non-uniformly sampled, and comprise low frequency activities and recurring patterns. Storing and making sense of such data in a typical database management system is difficult, due to the impedance mismatch between classical (e.g., relational) data and tricklets. In this paper, we investigate the management of large amounts of tricklets from an architectural perspective, and propose MiSTRAL (MaSsive TRicklets anALysis), an architecture designed for executing low-latency analytics on time series warehouses. MiSTRAL uses a dictionary based representation for tricklets that allows queries to be run natively on compressed representations and thus to achieve the low-latency goal. The architecture of MiSTRAL is presented in detail in the following, along with early experimental results on several Smarter Cities datasets.

Online Analytical Processing (OLAP) is a popular technique for explorative data analysis. Usually, a fixed set of dimensions (such as time, place, etc.) is used to explore and analyze various subsets of a given, multi-dimensional data set. These subsets are selected by constraining one or several of the dimensions, for instance, showing sales only in a given year and geographical location. Still, such aggregates are often not enough. Important information can only be discovered by combining several dimensions in a multidimensional analysis. Most existing approaches allow to add new dimensions either statically or dynamically. These approaches support, however, only the creation of global dimensions that are not interactive for the user running the report. Furthermore, they are mostly restricted to data clustering and the resulting dimensions cannot be interactively refined.

In this work we propose a novel, sound framework for evolutionary feature selection in unsupervised machine learning problems. We show that unsupervised feature selection is inherently multi-objective and behaves differently from supervised feature selection in that the number of features must be maximized instead of being minimized. Although this might sound surprising from a supervised learning point of view, we exemplify this relationship on the problem of data clustering and show that existing approaches do not pose the optimization problem in an appropriate way. Another important consequence of this paradigm change is a method which segments the Pareto sets produced by our approach. Inspecting only prototypical points from these segments drastically reduces the amount of work for selecting a final solution. We compare our methods against existing approaches on eight data sets.

KDD is a complex and demanding task. While a large number of methods has been established for numerous problems, many challenges remain to be solved. New tasks emerge requiring the development of new methods or processing schemes. Like in software development, the development of such solutions demands for careful analysis, specification, implementation, and testing. Rapid prototyping is an approach which allows crucial design decisions as early as possible. A rapid prototyping system should support maximal re-use and innovative combinations of existing methods, as well as simple and quick integration of new ones.This paper describes Yale, a free open-source environment forKDD and machine learning. Yale provides a rich variety of methods whichallows rapid prototyping for new applications and makes costlyre-implementations unnecessary. Additionally, Yale offers extensive functionality for process evaluation and optimization which is a crucial property for any KDD rapid prototyping tool. Following the paradigm of visual programming eases the design of processing schemes. While the graphical user interface supports interactive design, the underlying XML representation enables automated applications after the prototyping phase.After a discussion of the key concepts of Yale, we illustrate the advantages of rapid prototyping for KDD on case studies ranging from data pre-processing to result visualization. These case studies cover tasks like feature engineering, text mining, data stream mining and tracking drifting concepts, ensemble methods and distributed data mining. This variety of applications is also reflected in a broad user base, we counted more than 40,000 downloads during the last twelve months.