A Hierarchical Storage System for Industrial TimeSeries Data The increasing interest among manufacturers in monitoring and analyzing industrial systems is generating a problem related to the considerable costs associated with the storage of captured data. This paper presents a threelevel hierarchical architecture for timeseries data storage on cloud environments that helps to decrease those costs. In the first level, new raw timeseries data is stored for a shortperiod of time (e.g., one day) on electronic nonvolatile storage such as solidstate drives (SSDs) that provide fast access for real time visualization of the latest data. In the second level, recent time series are stored for a mediumperiod of time (e.g., one week) on magnetic hard disk drives (HDDs) that are lowercost devices with slower data transfer speed. In the third level, a reduced representation of the time series obtained by applying timeseries reduction techniques are stored in HDDs, for a longer period of time (e.g., one year). Dealing with those reduced representations, data storage and transmission costs can be decreased, without limiting the future use of the data in different processes.The architecture has been implemented by using the top Database Management System from three different categories: Wide column store, Time series DBMS and Graph DBMS. It has been tested using industrial time series coming from a real manufacturing environment, and with three different types of queries proposed by domain experts. The performance results regarding storage space, storage costs and query time processing are shown on the paper.

Monitoring Data Management Services on the Edge Using Enhanced TSDBs Many IoT systems are data intensive and are for the purpose of monitoring of critical systems. In these monitoring systems, a large volume of data steadily flow out of a large number of sensors which monitor the physical systems and environments. Thus, first of all, we need to consider how to store and manage these IoT data. Also, data sharing can greatly enhance the quality of data analytics and help with cold start of similar systems. Thus, the data storage and management solutions should consider how to help discover useful data in order to facilitate data sharing. Time series databases (TSDBs) have been developed in recent years for storing IoT data, but they have some deficiencies. One problem is that they are not very effective in supporting data sharing due to the lack of a good semantic model for proper data specifications, which is critical in data discovery. To resolve this problem, we develop a monitoring data annotation (MDA) model to guide the systematic specification of monitoring data streams. To support the realization of the MDA model, we also develop an external tool suite, which stores the additional MDAbased specifications for the data streams and interfaces with queries to perform preliminary processing to allow effective monitoring data discovery based on the MDA specifications. Another problem with current TSDBs is their focus on storing time series data that arrive at a fixed rate, but not on storing and retrieval of event data, which may come sporadically with irregular timing patterns. When storing such event data in existing TSDBs, the retrieval may have performance problems. Also, existing TSDBs do not have specific query language defined for event analysis. We develop a model for event specifications and use it to specify abnormal system states to be captured to allow timely mitigation. The event model is integrated into the TSDB by translating them to continuous queries defined in some TSDBs. Also, we develop an event storage scheme and incorporate it in TSDBs to facilitate efficient event retrieval. Experimental results show that our event solution for the TSDB is effective and efficient.