Time Series Management Systems:A Survey

The collection of time series data increases as more monitoring and automation are being deployed. These deployments range in scale from an Internet of things (IoT) device located in a household to enormous distributed Cyber-Physical Systems (CPSs) producing large volumes of data at high velocity. To store and analyze these vast amounts of data, specialized Time Series Management Systems (TSMSs) have been developed to overcome the limitations of general purpose Database Management Systems (DBMSs) for times series management. In this paper, we present a thorough analysis and classification of TSMSs developed through academic or industrial research and documented through publications. Our classification is organized into categories based on the architectures observed during our analysis. In addition, we provide an overview of each system with a focus on the motivational use case that drove the development of the system, the functionality for storage and querying of time series a system implements, the components the system is composed of, and the capabilities of each system with regard to Stream Processing and Approximate Query Processing (AQP). Last, we provide a summary of research directions proposed by other researchers in the field and present our vision for a next generation TSMS.Comment: 20 Pages, 15 Figures, 2 Tables, Accepted for publication in IEEE TKD

Probabilistic short-term load forecasting at low voltage in distribution networks

Predmet istraživanja ove doktorske disertacije je kratkoročna probabili- stička prognoza opterećenja na niskom naponu u elektrodistributivnim mre- žama. Cilj istraživanja je da se razvije novo rešenje koje će uvažiti varija- bilnost opterećenja na niskom naponu i ponuditi konkurentnu tačnost prog- noze uz visoku efikasnost sa stanovišta zauzeća računarskih resursa. Predlo- ženo rešenje se zasniva na primeni statističkih metoda i metoda mašinskog (dubokog) učenja u reprezentaciji podataka (ekstrakciji i odabiru atributa), klasterovanju i regresiji. Efikasnost predloženog rešenja je verifikovana u studiji slučaja nad skupom realnih podataka sa pametnih brojila. Rezultat primene predloženog rešenja je visoka tačnost prognoze i kratko vreme izvr- šavanja u poređenju sa konkurentnim rešenjima iz aktuelnog stanja u oblasti.This Ph.D. thesis deals with the problem of probabilistic short-term load forecasting at the low voltage level in power distribution networks. The research goal is to develop a new solution that considers load variability and offers high forecasting accuracy without excessive hardware requirements. The proposed solution is based on the application of statistical methods and machine (deep) learning methods for data representation (feature extraction and selection), clustering, and regression. The efficiency of the proposed solution was verified in a case study on real smart meter data. The case study results confirm that the application of the proposed solution leads to high forecast accuracy and short execution time compared to related solutions

PhilDB: the time series database with built-in change logging

PhilDB is an open-source time series database that supports storage of time series datasets that are dynamic; that is, it records updates to existing values in a log as they occur. PhilDB eases loading of data for the user by utilising an intelligent data write method. It preserves existing values during updates and abstracts the update complexity required to achieve logging of data value changes. It implements fast reads to make it practical to select data for analysis. Recent open-source systems have been developed to indefinitely store long-period high-resolution time series data without change logging. Unfortunately, such systems generally require a large initial installation investment before use because they are designed to operate over a cluster of servers to achieve high-performance writing of static data in real time. In essence, they have a ‘big data’ approach to storage and access. Other open-source projects for handling time series data that avoid the ‘big data’ approach are also relatively new and are complex or incomplete. None of these systems gracefully handle revision of existing data while tracking values that change. Unlike ‘big data’ solutions, PhilDB has been designed for single machine deployment on commodity hardware, reducing the barrier to deployment. PhilDB takes a unique approach to meta-data tracking; optional attribute attachment. This facilitates scaling the complexities of storing a wide variety of data. That is, it allows time series data to be loaded as time series instances with minimal initial meta-data, yet additional attributes can be created and attached to differentiate the time series instances when a wider variety of data is needed. PhilDB was written in Python, leveraging existing libraries. While some existing systems come close to meeting the needs PhilDB addresses, none cover all the needs at once. PhilDB was written to fill this gap in existing solutions. This paper explores existing time series database solutions, discusses the motivation for PhilDB, describes the architecture and philosophy of the PhilDB software, and performs an evaluation between InfluxDB, PhilDB, and SciDB