Data Historians in the Data Management Landscape

Abstract

At EDF, a leading energy company, process data produced in power stations are archived both to comply with legal archiving requirements and to perform various analysis applications. Such data consist of timestamped measurements, retrieved for the most part from process data acquisition systems. After archival, past and current values are used for various applications, including device monitoring, maintenance assistance, decision support, statistics publication, etc. Large amounts of data are generated in these power stations, and aggregated in soft real-time -without operational deadlines -at the plant level by local servers. For this long-term data archiving, EDF relies on data historians -like InfoPlus.21, PI or Wonderware Historian -for years. This is also true for other energy companies worldwide and, in general, industry based on automated processes. In this paper, we aim at answering a simple, yet not so easy, question: how can data historians be placed in the data management landscape, from classical RDBMSs to NoSQL systems? To answer this question, we first give an overview of data historians, then discuss benchmarking these particular systems. Although many benchmarks are defined for conventional database management systems, none of them are appropriate for data historians. To establish a first objective basis for comparison, we therefore propose a simple benchmark inspired by EDF use cases, and give experimental results for data historians and DBMSs.

Proposal for an IIoT Device Solution According to Industry 4.0 Concept

“…Data are collected from one process or from the entire complex of devices in the production line. Data, available from the process control, make it possible to detect the performance requirements and to identify control areas where IIoT devices could be deployed [ 68 , 69 ].…”

Identifying Data Dependencies as First Step to Obtain a Proactive Historian: Test Scenario in the Water Industry 4.0

“…No Historian was encountered in the water industry that used accumulated data to automatically identify data dependencies, that elaborated on optimizing strategy-related conclusions, nor that reacted over local process controls to increase its efficiency in any way. The currently implemented Historians in the water industry were not aware of interfaced process characteristics; therefore, they cannot have any process-related objective [52,53].…”

Chronos: a NoSQL system on flash memory for industrial process data

“…The full specifications of this benchmark are given in [13] along with a more thorough analysis of different solutions; this paper focuses on Chronos performance. To sum up, this benchmark defines 12 kinds of queries: insertion (Q0), update (Q1), raw data extraction (Q2), aggregate calculation (Q3, Q4, Q5 and Q6), data filtering (Q7 and Q8), analysis on multiple time series (Q9 and Q10) and most recent value extraction for all time series (Q11).…”

“…We also evaluate the NoSQL system Berkeley DB version 5.2, whose access methods and usage are comparable with Chronos. MySQL and Berkeley DB have been tuned to improve their performance [13], especially by disabling transactions. On the other hand, In-foPlus.21 and Chronos are designed for this application, and did not need any significant adjustment.…”

“…Insert operations also benefit from the fill factor optimization. Accessing single tuples, i.e, non range queries, (updates and current values extraction) involve random accesses for every solution, which then exhibit comparable performance -with the notable exception of InfoPlus.21 which is optimized for current values extraction [13].…”