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.

We present IQR, a system that demonstrates optimization based interactive relaxations for queries that return an empty answer. Given an empty answer, IQR dynamically suggests one relaxation of the original query conditions at a time to the user, based on certain optimization objectives, and the user responds by either accepting or declining the relaxation, until the user arrives at a non-empty answer, or a non-empty answer is impossible to achieve with any further relaxations. The relaxation suggestions hinge on a proba- bilistic framework that takes into account the probability of the user accepting a suggested relaxation, as well as how much that relaxation serves towards the optimization objec- tive. IQR accepts a wide variety of optimization objectives - user centric objectives, such as, minimizing the number of user interactions (i.e., effort) or returning relevant results, as well as seller centric objectives, such as, maximizing profit. IQR offers principled exact and approximate solutions for gen- erating relaxations that are demonstrated using multiple, large real datasets.

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.

We propose a principled optimization-based interactive query relaxation framework for queries that return no answers. Given an initial query that returns an empty answer set, our framework dynamically computes and suggests alternative queries with less conditions than those the user has initially requested, in order to help the user arrive at a query with a non-empty answer, or at a query for which no matter how many additional conditions are ignored, the answer will still be empty. Our proposed approach for suggesting query relaxations is driven by a novel probabilistic framework based on optimizing a wide variety of application-dependent objective functions. We describe optimal and approximate solutions of different optimization problems using the framework. We analyze these solutions, experimentally verify their efficiency and effectiveness, and illustrate their advantage over the existing approaches.

Outlyingness is a subjective concept relying on the isolation level of a (set of) record(s). Clustering-based outlier detection is a field that aims to cluster data and to detect outliers depending on their characteristics (i.e. small, tight and/or dense clusters might be considered as outliers). E xisting methods require a parameter standing for the “level of outlyingness”, such as the maximum size or a percentage of small clusters, in order to build the set of outliers. Unfortunately, manually setting this parameter in a streaming environment should not be possible, given the fast time response usually needed. In this paper we propose Wod, a method that separates outliers from clusters thanks to a natural and effective principle. The main advantages of Wod are its ability to automatically adjust to any clustering result and to be parameterless.