=Paper=
{{Paper
|id=Vol-2790/paper24
|storemode=property
|title=
Improvement of the Data Quality Assessment Procedure in Large Collections of Spectral Data
|pdfUrl=https://ceur-ws.org/Vol-2790/paper24.pdf
|volume=Vol-2790
|authors=Alexey Akhlestin,Nikolai Lavrentiev,Natalya Lavrentieva,Alexey Kozodoev,Elena Kozodoeva,Alexey Privezentsev,Alexander Fazliev
|dblpUrl=https://dblp.org/rec/conf/rcdl/AkhlestinLLKKPF20
}}
==
Improvement of the Data Quality Assessment Procedure in Large Collections of Spectral Data
==
https://ceur-ws.org/Vol-2790/paper24.pdf
Improvement of the Data Quality Assessment
Procedure in Large Collections of Spectral Data
Alexey Akhlestin, Nikolai Lavrentiev, Alexey Kozodoev,
Elena Kozodoeva, Alexey Privezentsev, Alexander Fazliev[0000-0003-2625-3156]
V. E. Zuev Institute of Atmospheric Optics SB RAS, Tomsk, Russia
lexa@iao.ru, lnick@iao.ru, kav@iao.ru, faz@iao.ru,
remake@iao.ru, klen@iao.ru
Abstract. Relations between the components of the data layer and application
layer in the quantitative spectroscopy are schematically represented. The
sequence of actions in the data quality analysis in the W@DIS information
system is described. Methods for the spectral data quality analysis and
assessment of trust in expert data sources and the results of their application in
the W@DIS information system are presented. Along with common techniques
for the primary data source quality analysis, techniques for decomposition of
expert data sources and pairwise comparison of ordered data sets are reviewed.
We also discuss the use of empirical data for filtering large data collections by
an acceptable difference between identical energy levels in primary data sources
and in an empirical data source. Two types of the user interface for viewing the
results of spectral data analysis and assessment of trust in expert data are
considered. Original methods for the data source quality analysis and assessment
of trust in expert data are briefly described. These methods are used for finding
discrepancies between different spectral data types (primary, expert, and
empirical).
Keywords: Molecular Spectroscopy, Spectral Resources, Quality Control of
Spectral Data
1 Introduction
Assessment of the quality of information resources located on the Internet and
representation of these resources in the form of software agents convenient for work
are among currently important problems. Semantic Web (SW) technologies [1] were
suggested for their solution. The tools for representing semantic annotations of
resources turned out to be universal, while the techniques for assessment of the quality
of resources, at least for scientific subject domains, are not universal.
Assessments of the quality of scientific resources can be divided into two groups. In
the first group, the reliability of scientific resources connected with a mathematical
model of a subject domain is assessed. For the validity check, criteria are used derived
from the constraints imposed by the model on the entities described in the subject
domain. In the second group, trust in resources is assessed, in particular, the influence
of experts, presenting these resources.
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
263
When studying the states and transitions of molecules and atoms, large-volume
numerical arrays are used; some of them contain parameters of several billion
transitions for a molecule.
Virtual Atomic and Molecular Data Centre (VAMDC) [2-4] is currently the most
famous spectral data center. It consolidates about 30 organizations and includes 39
databases, which include atomic (8 databases), molecular (22), and solid-state
spectroscopy (2) data. The remaining DBs contain information about the kinetics of
photochemical processes, collisions of molecules, atoms, and electrons, absorption
cross sections during photo dissociation, and dissipative processes during electron
collisions. Most of the atomic and molecular databases are components of information
systems (IS) with web interfaces. All atomic molecular spectroscopy ISs provide
researchers with lists of unique transitions, which can contain calculated transition
parameters or mixtures of calculated and measured parameters. The quality of data in
these systems is checked and assessed by the authors by way of comparison of their
own results with those of other researchers. However, the results of these comparisons
are described very briefly and selectively. The lack of explicit representation of the data
quality analysis results and incomplete information about the analysis make it necessary
to re-assess the data quality. This reassessment is time consuming and labor-intensive.
Therefore, researchers have to decide based on the authoritative trust criterion in most
cases. The disadvantage of the VAMDC site is the inaccessibility of the information
resource quality analysis results.
Information system W@DIS [5] is a part of VAMDC; it includes primary, expert,
and empirical spectral data and provides a complete description of the results of their
quality assessment. These data describe more than hundred molecules (including
isotopologues). A feature of W@DIS IS is the presence of complete results of the
physical parameters and data sources quality analysis for all spectral data contained in
it. All results of quality assessment are presented in the form of ontologies [6–9].
This paper continues the cycle of our works [10-12] on the study of various aspects
of the analysis of the quality of spectral data. The second half of this paper partially
overlaps with the extended abstract of the report [13]. In this work, we discuss the
sequence of application of different methods for assessment of spectral information
quality, features of these methods, and user interfaces for handling the results of
spectral information quality analysis.
2 Data Model in Quantitative Spectroscopy. Data Quality
Control
Before consideration of a data model for quantitative spectroscopy, we should
emphasize the fact that the model suggested does not describe all facts related to the
subject domain under study. However, it includes most data used in applied subject
domains. The data model describes three data types.
Data related to measurements or calculations published in a particular work and their
brief description together are called the primary data source. Primary experimental and
theoretical data are interrelated, because not all values of the properties of transitions
264
and states are measurable, for example, quantum numbers, the values of which can be
determined only by solving computational problems. In addition to primary data,
composite data are also used in spectroscopy. Two composite data types are important
for applications: empirical and expert data. The former are calculated by using a
multiset of measured data, and the latter are a set of calculated, measured, and reference
data. Thus, the data layer contains three data types: measurement data and their
derivatives (empirical energy levels calculated on the basis of the Rydberg–Ritz
principle [14]), calculation data, and expert data, which are a mixture of the first and
second data types.
Figure 1 shows three groups of data and their relationship with experiment and
calculations. Each data group is marked by a rounded rectangle; and the applications
where these data are found, by rectangles. These applications are connected with six
problems of quantitative spectroscopy [15] and the problem of expert data sets
construction. The quality of the data included in three datasets (multiset of measured
transitions and predicted and reference transitions) can only be assessed by formal
criteria (validity), while expert datasets should be tested in accordance with trust
criteria, since formal methods for constructing such datasets are to be developed. The
computational method used for the formalization of data processing for constructing
expert datasets in W@DIS was described in [16–18].
Since the sources of spectral information in the form of expert datasets are in great
demand, expert data are also of interest in this work. There are three reasons for this.
Firstly, the expert data on applied subject domains, the number of which exceeds
several dozen, are contradictory. Note that data for a particular subject domain must
meet certain requirements for their quality. Secondly, expert data are formed via
informal manipulations, where the professional skills and preferences of experts play
an important role. This is why different forms of publication criteria are a useful tool
for the expert data quality assessment. Thirdly, the values derived from measurements
and calculations are adapted when forming expert data used in solution of applied
problems.
The analysis of the quality of C0 — the multiset of transitions characteristics of which
are measured and published for each molecule takes the central place in the data
analysis. This set consists of several parts. The part C0A includes incorrectly identified
transitions which are excluded from the quality analysis. The part C consists of
correctly identified transitions, but can contain conflicting values of wavenumbers and
other characteristics for identical transitions. For a number of molecules, Cantor sets of
states (sets with all elements unique) are composed based on the multiset of transitions
measured. The states in these sets are described by empirical energy levels E R (for
example, for a water molecule [18]), which can be used for the check of the
corresponding set of transitions C. For this, all possible transitions (CR), identical to the
transitions from the set C, should be constructed from the empirical E R levels. These
transitions, which are identical to the transitions from CR, form the set of transitions
CiR. It consists of two subsets Ai and Ai, which include the transitions from C with the
wavenumbers, differing from those of identical transitions from CR by less or more than
Δ cm–1, respectively. Here Δ is the maximum permissible deviation between the
wavenumbers of transitions from the set CiR and of identical transitions from the set CR.
265
Figure 1. Schematic representation of the relationship between applications and associated
input and output data [8].
Figure 2 shows the structure of the C0 set. Let us write the obvious equalities: CiR =
Ai U Ai, C = CA U CiR, and С0 = C U C0A (U is the union of sets).
Figure 2. Structure of the multiset of transitions derived from the data quality analysis in the
quantitative spectroscopy
The numbers of elements in the sets CiR and CA do not change at a fixed number of the
elements in the multiset C, but the number of elements in the sets Ai and Ai depends on
Δ. This parameter is used as a quantitative measure of the permissible difference
between the values of a wavenumber which describes a transition.
The quality analysis of data related to transitions from the set C A is of greatest
interest. If this set is Cantor, then a possibility of compiling a set of empirical energy
levels from it is doubtful. Therefore, it is necessary to carry out measurements to expand
the number of empirical energy levels in the set CiR.
The main information resources in W@DIS are sources of data on states and
transitions. Each data source includes an uploaded numerical array or plot published
and the properties of this array (plot).
Let D0n and D0Em denote the primary array of measured values of physical parameters
and the expert data array, respectively. Figure 3 shows the order of the quality analysis
266
of an array uploaded (D0n or D0Em). Note that the above introduced multiset of
transitions is the union of arrays:
C0 = UNn=1 D0n. (1)
Hence, the equalities
C = UNn=1 Dn, C0A = UMm=1 Dm, etc. (2)
are true.
The ellipses in Fig. 3 show the parts of the dataset related to the parts of the multiset.
The boxes represent applications which perform calculations during the data quality
analysis. The in (out) arrows to a rectangle are associated with the input (output) of
these applications. Plus and minus signs indicate that the output satisfies (+) or does
not satisfy (-) the constraints of an application.
The difference in the data quality analysis for expert data in comparison with the
previous one is the appearance of the Decomposition application during the second
stage. This application outputs two data sets: DEDn which includes physical parameter
values close to published, and DAEDn which include values which significantly differ
from published ones. It is possible to work with both data groups obtained from
decomposition in W@DIS. CC is the set of primary computed transitions that is used
during decomposition.
The data quality analysis results are part of the metadata for each data source in the
W@DIS system. This metadata are represented in two forms: human readable and
ontology for software agents.
3 General Description of the Collection of Spectral Line
Parameters
The spectral data quality is analyzed in each paper on quantitative spectroscopy with
the use of traditional methods. The division of spectral data into data sets, which relate
to individual molecules and solutions to one of the seven spectroscopy problems in the
W@DIS information system (IS) (http://wadis.saga.iao.ru) [15] allowed us to simplify
the primary data analysis and to assess trust in expert data used in different application
problems. Existing expert data created by different experts are contradictory. To resolve
the contradictions, a decomposition method has been suggested to assess the trust in
these data [10, 19]. The methods for the data quality analysis and assessment of trust in
expert spectral data are briefly described in the report. Spectral data collections in
W@DIS are associated with the output data of several dozens of atmospheric molecules
[5]. The structure of a collection is identical to the structure of physical problems solved
in molecular spectroscopy and includes data on energy levels, vacuum transitions, and
spectral parameters of lines of individual molecules and their mixtures. Each part of a
collection is assigned to the publication from which these data has been extracted.
267
Along with data sources, the system includes all publications related to data arrays
accumulated in the collections.
Each data source is connected with a conventional set of metadata, which describes
the set of properties of this source. Data on different molecules are not connected. This
fact determines the branched modular structure of ontologies, each describing one of
the modules. Every molecular collection is described by ontologies, which
characterizes the information resources or physical quantities in this collection.
The main properties of data sources are those which describe the source quality analysis
results. Data sources in the collections are typified. The following types are used for
the classification: primary (measurements or calculations), expert, and empirical. The
first three types are traditional for natural science data collections, while empirical data
in different subject domains can be formed according to different principles. In
spectroscopy, empirical data includes data related to energy levels and other physical
quantities. Empirical energy levels are obtained from processing the complete set of
energy levels derived from the inverse problem solution accounting the Rydberg–Ritz
principle [9].
Authors of most publications use traditional methods for the analysis of spectral data
quality: check of selection rules and calculation of standard deviations. When working
with data collections, we use original methods for the data quality analysis and
assessment of trust in expert data. These methods are briefly described below.
4 Methods of Spectral Data Quality Analysis
These methods can be divided into traditional and new [8, 10, 11]; we have introduced
the latter for controlling the quality of primary and expert data. They are briefly
described below.
4.1 Traditional Methods for Spectral Data Quality Analysis
During the analysis of spectral data quality, the states and transitions with incomplete
set of quantum numbers are first distinguished. The second group of data excluded from
the consideration includes transition duplicates in a data source.
The values of quantum numbers which characterize the energy levels and transitions
between energy levels in a molecule are limited by the selection rules which follow
from the mathematical model of a molecule. In W@DIS, forbidden transitions are
marked and do not participate in any operations with data sources.
Paired relationships between data sources are estimated by the standard deviation or
maximal deviation between the values of a physical quantity, identical states, or
identical transitions compared. The acceptable deviation is determined by a researcher
which uses the data to solve a problem.
268
a
b
Figure 3. Sequence of actions in spectral data quality analysis: (a) measured data and (b) expert data
269
4.2 The Use of Empirical Data for the Collection Quality Control
Along with the above described methods, there is an approach which uses the empirical
data calculated and allows improvement of the quality of spectral data in the collections.
Such empirical data have been acquired for several dozens of molecules. This method
is currently applied to data on all isotopologues of the water molecule in W@DIS. The
allowable difference determined by an expert (0.035 cm-1) between the energy levels
of identical states taken from the data source, checked for quality, and from an empirical
data source forms a filtering criterion. The results of application of this criterion are
shown in Fig. 6.
The efficiency of this method depends on the number of states with empirical values
of the energy levels ER. The efficiency is maximal when the set CA is empty.
4.3 Pairwise Comparison of Ordered Spectral Data Arrays
The method [15] is used for identification of qualitative contradictions related to the
sequence of states or transitions identified. It is based on two assumptions: the set of
ideal states (transitions) is a Cantor set and it determines the ideal order of states
(transitions); ideal values of physical quantities imply the ideal order of states
(transitions). Three disorder factors [10] are calculated in the method, which allow one
to estimate the different causes of arising contradictions. The software implementation
of the method and its practical application faced the problem of long time required for
the comparison of arrays of calculated data, including several billion transitions. The
time required for the calculation of the factors exceeds several years.
Our recent studies have shown that the need in this method arises in the cases where
traditional analysis techniques give a satisfactory quantitative result, but doubts remain
about the degree of accuracy. In other words, this method should be applied in the cases
where, for example, the spectral line centers should be determined with high accuracy
in narrow spectral ranges.
4.4 Assessment of Trust in Expert Spectral Data
The above methods for the data quality analysis are applied to the primary data derived
from measurements and calculations. Expert data are generated according to informal
criteria, including subjective criteria, expediency, etc. Therefore, these data require
another method for qualitative assessment. The method of expert data array
decomposition we use is based on the following assumption: expert data should not
differ significantly from the full set of primary data published. The decomposition by
complete consistent (rigorous) or inconsistent set (non-rigorous method) is admissible.
In spectroscopy, the non-rigorous method is required during the initial stage of study
of spectral properties of molecules when there is no sufficiently good model or accurate
measurement data. A researcher must choose numerical values based of the
requirements for the task he solves.
270
Figure 4. Interface for viewing metadata of a data source. The left part contains the analysis results for binary relationships between data sources; the
right bottom part contains the analysis results for selection rule checking.
271
a
b
Figure 5. Fragments of the interface for viewing the analysis results for paired relationships
(one–to–many) between data sources.
5 Interfaces for Viewing Data Quality Analysis Results
Two sets of interfaces are created in W@DIS for viewing the data quality analysis
results. The first interface is intended for viewing metadata of a data source selected
and its correlations with all other sources. The second interface is designed for
description of paired relationships for the entire collection related to a molecule.
272
5.1 Comparison of a Selected Data Source with all the Sources which Include
Identical States or Transitions (One-to-Many)
Figure 4 shows the interface for viewing metadata of expert data source [19]. It consists
of four parts. A reference and a molecule are described in the upper part on the left, and
physical quantities are described on the right. The bottom part of the interface shows
the interfaces for viewing correlations between data sources and assessment of trust in
the expert data array for the H216O molecule (on the left). A more detailed description
is given in Fig. 5.
This comparison is carried out when describing a data source selected, but it can also
be performed as refinement of the comparison (many–to–many) when describing a
collection. Figure 4a shows that there are no forbidden transitions according to the
selection rule checking; 6410 transitions were not identified. Fig. 5b shows a part of
the list (comparison was made with 171 data sources), where the results disagree with
the calculation data [20]. The discrepancy with the expert data array [21] is much
smaller; the quantitative agreement with the following four works is good, while the
ordering factors are nonzero and one line in the spectrum is disordered [22].
5.2 Complete Pairwise Comparison of Data Sources that Have Identical States or
Transitions (Many–to-Many)
The results of comparison of all data sources can be presented in one viewing interface
with quantitative characteristics or without them, i.e. qualitatively. Such a qualitative
representation is shown in Fig. 6a, where pairs of sources are shown in red if the
criterion chosen by a researcher is violated. This representation allows one to visually
assess the number of good or bad pairs. Fig. 6b shows a fragment of the interface, where
the quantitative coincidence of the data compared is obvious.
Figure 6a shows a part of a symmetric matrix with the qualitative results of
estimation of the maximal difference between the frequencies in two published arrays
of transitions, which are to be compared and include parameters of identical transitions,
in the cells. If the maximal difference is less than 0.035 cm–1, then the corresponding
cell is light gray; if the cell is white, this means that the compared arrays have no
identical transitions, and if the cell color is dark gray, then the maximal difference is
higher than 0.035 cm–1. Note that the number of cells with the high difference is quite
large (slightly less than half of all data). The arrays of values published have passed the
first stage of data filtering.
11
273
a
b
Figure 6. Interface for viewing the analysis results for paired relationships (many–to–many)
between data sources (comparison of sources from the methane molecule data collection): (a)
qualitative presentation (pairs which do not satisfy the criterion are shown in dark gray (red));
(b) fragment of the interface with matrix quantitative representation (pairs which satisfy the
criterion are shown in light gray (yellow)); the first number is the maximal difference, and the
second is the number of identical transitions in a pair.
5.3 The Use of Empirical Data to Control the Quality of the Collection of
Spectral Data on the Water Molecule
Figure 7 shows the quality analysis results for the multiset of H2O transitions measured,
which have been tested by the criterion of maximal deviation between the values of
wavenumbers of a pair of identical transitions Δ < 0.035 cm-1.
12
274
Figure 7. The analysis results for paired relationships (many-to-many) between data sources
(qualitative comparison of the sources of data collection on the water molecule)
Table 1. Number of transitions in different parts of the multiset C0.
The number of
Molecule H216O
measured transitions
Total of which are
Transition groups and associated arrays
transitions unique
C0 - The number of transitions uploaded to the 312366 -
collection
C0A - Transitions with incorrect quantum numbers 19932 -
C - Transitions with correct quantum numbers 292434 103324
CA - Transitions in which at least one state (upper or 1400 837
lower) is in ER
CiR - Transitions in which both states (upper and 291034 102487
lower) are in ER
Ai - Identical transitions for which |ωCiR – ωСR| < Δ 281561 100270
Ai - Identical transitions for which |ωCiR – ωСR| > Δ 9473 5302
Figure 7 shows the results of comparison of the same data arrays, but after the second
stage of filtering, during which transitions with the wavenumbers from the C array
13
275
which differed from the corresponding wavenumbers of the identical transitions in the
CiR array were rejected. The number of dark gray cells in Fig. 7 is much less than in
Fig. 6a.
Table 1 presents number of transitions in different parts of the multiset C0. They are
calculated for the transitions of the main isotopologue of the water molecule. In the
table one can check the following equalities from Figure 2: the number of transitions in
C is equal to the sum of transitions in the sets C A and CiR and the number of unique
transitions in C is equal to the sum of unique transitions in the sets CA and CiR.
Details of the study on improvement of the quality of data on the water molecule are
given in [23]. Here, we present only the figure which characterizes the collection data
quality before and after the filtration.
5.4 Interface for Viewing the Results of Assessment of Trust in Expert Data
Fig. 8 shows a fragment of the interface which represents a part of the results of trust
assessment. This part characterizes the decomposition results for a complete set of
computed and measured data.
Figure 8. A fragment of the interface for viewing the results of the assessment of trust in the
expert HITRAN data array [20].
The expert source contains 135606 transitions in total. The lines “Distrusted Vacuum
Wavenumbers” characterize the number of transitions with low trust in different
spectral ranges and over the entire spectral region. These transitions make 8% of the
total number of transitions. Most low-trust transitions (87%) are in the near-IR and
visible regions.
14
276
Conclusions
We describe a collection of spectral data in W@DIS information system and the
methods used in it for the analysis of spectral data quality. A data model in quantitative
spectroscopy and a group of applications for data acquisition are presented. Three data
groups are distinguished: data derived from measurements, from calculations, and their
combination in the form of expert spectral data. Different parts of the multiset of
transitions, which consists of numerical arrays extracted from publications, are
described. The sequence of actions for the data quality analysis is explained. The
quality assessment techniques commonly used in spectroscopy are briefly described, as
well as three methods for the spectral data quality analysis developed by the authors for
large arrays of conflicting spectral data. For better visual representation of the results,
graphical user interfaces for working with the results of data quality assessment are
exemplified. The percentage ratio of the parts of the multiset is shown, with the multiset
of transitions of the main water molecule isotopologue as an example. In particular, we
have shown that less than 1.5% of the data remains after data filtering in the automatic
spectral data quality analysis, which require additional processing by experts.
Acknowledgement. The main part of the work was performed within project №
АААА-А17-117021310147-0.
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