1

How can I put a more comprehensive table? The table is vertical. I want it to occupy the entire page height so that all columns have the same space and respect the text. The table is too small.

Link to the code: https://www.overleaf.com/read/sdtpsrdymqrj

Code:

\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage{amsmath,amsfonts}
\usepackage{algorithmic}
\usepackage{algorithm}
\usepackage{array}
\usepackage[caption=false,font=normalsize,labelfont=sf,textfont=sf]{subfig}
\usepackage{textcomp}
\usepackage{stfloats}
\usepackage{url}
\usepackage{verbatim}
\usepackage{graphicx}
\usepackage{cite}
\hyphenation{op-tical net-works semi-conduc-tor IEEE-Xplore}
% updated with editorial comments 8/9/2021
%\User defined packages
% highlight
\usepackage{color,soul}
\DeclareRobustCommand{\hlc}[1]{{\sethlcolor{lightgray}\hl{#1}}}
% todonotes
\usepackage{todonotes}
% degree symbol
\usepackage{gensymb}
% tables
\usepackage{caption}
\usepackage{csvsimple}
\usepackage{booktabs, tabularx, wrapfig}
\usepackage{array, ltablex, multirow}
\usepackage{placeins}
\usepackage{graphicx}
%\usepackage{subcaption}
\usepackage{float} % added
\usepackage[nonumberlist,nogroupskip]{glossaries}
%------ tables
\usepackage{adjustbox}
\usepackage{rotating}
\usepackage{makecell}
\usepackage{xcolor}
    \colorlet{bgodd}{black!10}
\usepackage{tabularray}
     \UseTblrLibrary{booktabs}
\usepackage{pdflscape}
%------

\begin{document}


\begin{table}[ht]
    \caption{Test}
    % from makecell
    \settowidth\rotheadsize{\small Monitoring-Based}
    \scriptsize
    \setlength\tabcolsep{0.1mm} % let LaTeX calculate intercolumn whitespace
    \rotatebox{90}{
        \begin{tblr}{
        colspec = {Q[l, wd=5cm] *{3}{X[c]} *{10}{X[c]}},
        vline{2-Y} = {2-Z}{dotted},
        vline{2,5} = {2-Z}{solid, \lightrulewidth},   % \lightrulewidth is defined in booktabs
        rows = {abovesep=2pt, belowsep=2pt},
        row{odd} = {bg=bgodd},
        colsep = 2pt,
        row{1} = {
            font=\bfseries, %\linespread{0.84}\selectfont,
            c, m,
        },
        row{2} = {
              cmd=\rotcell,
            rowsep=0pt
        },
        }
        \toprule
        \SetRow{bg=white}
        Paper(s) &
        \SetCell[c=3]{c, m} {Data\\ Sources} &&&
            \SetCell[c=10]{c, m} Technique &&&&&&&&& \\
        \midrule
        & Log-based
            & Distributed Tracing-based
            & Monitoring-Based
            & {Unsupervised\\ learning}
            & {Supervised\\ learning}
            & Reinforcement learning
            & Semi-supervised learning
            & Hybrid learning
            & {Statistical\\ Approach}
            & Causal Inference
            & {Trace\\ comparison}
            & Heart Beating
            & SLO checks \\
        \midrule
        \cite{liu2020unsupervised, pahl2018all, jin2020anomaly, bogatinovski2020self} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        \textbullet &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
           % SLO checks 
        \\
        \cite{nedelkoski2019anomaly, gan2019leveraging, zhou2019latent} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        \textbullet &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{wang2020workflow, chen2020framework, meng2021detecting} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        \textbullet &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{li2021microservice} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        \textbullet &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{chow2014mystery} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        \textbullet &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{belhadi2021reinforcement} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        \textbullet &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{sharma2013cloudpd, zhang2016taskinsight, xu2018unsupervised, gulenko2018detecting, mariani2018localizing, wu2020microrca, wu2020performance, wang2018cloudranger, vallis2014novel, su2019robust, huang2013lof, bhaduri2011detecting,wang2012workload, lazarevic2003comparative} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        \textbullet &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{sauvanaud2018anomaly, liu2015opprentice, du2018anomaly, mariani2020predicting} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        \textbullet &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{magalhaes2010detection, peiris2014pad, abdelrahman2016detection, kang2012dapa, yang2007anomaly, wang2013energy, ahad2015toward, nguyen2013fchain, tan2012prepare, gu2009online, samir2019dla} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        \textbullet &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{wu2021causal, chen2014causeinfer, chen2016causeinfer, lin2018microscope} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        \textbullet &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{shan2019diagnosis} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        \textbullet  % SLO checks 
        \\
        \cite{zang2018fault} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        \textbullet  &   % HeartBeating
        % SLO checks 
        \\
        \cite{yagoub2018equipment, brown2018recurrent, nandi2016anomaly, jia2017logsed, fu2009execution, du2017deeplog} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        \textbullet &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks 
        \\
        \cite{fronza2013failure, zhang2016automated, zhang2019robust, liang2007failure} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        \textbullet &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks 
        \\
        \cite{meng2019loganomaly} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        \textbullet &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks 
        \\
       \cite{meng2019loganomaly, yang2021semi, li2021microservice} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        \textbullet &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks 
        \\
        \cite{salfner2007using, beschastnikh2014inferring} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        \textbullet &  % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks 
        \\
        \cite{he2020loghub} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        \textbullet &   % Hybrid learning
        &  % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks 
        \\
        \midrule
    \end{tblr}
    }
    {\label{tab:tbl_results}}
\end{table}



\end{document}

Thank you

===== Solution ======

Based on @Zarko's answer, I developed this solution :

\documentclass{article}
\usepackage{caption}
%------ tables
\usepackage{rotating}
\usepackage{makecell}
\usepackage{xcolor}
    \colorlet{bgodd}{black!10}
\usepackage{tabularray}
     \UseTblrLibrary{booktabs}
%------



\begin{document}

bla bla bla

\clearpage
\newpage

\clearpage
\newpage

\begin{sidewaystable*}[ht]
    
    \centering
    \caption{Papers analyzed in the survey grouped by data sources employed, and techniques used to detect anomalies.}
    %\caption{Papers analyzed in the survey grouped by data sources employed, and techniques used to detect anomalies}
    % from makecell
    \settowidth\rotheadsize{\small Monitoring-Based}
    \footnotesize
    \setlength\tabcolsep{0.1mm} % let LaTeX calculate intercolumn whitespace
    \begin{tblr}{
        width=\textheight-3\baselineskip,  % <--- added
        colspec = {l *{13}{X[c]}},
        vline{2-Y} = {2-Z}{dotted},
        vline{2,5} = {1-Z}{solid, \lightrulewidth}, % \lightrulewidth is defined in booktabs
        colsep = 1pt,                               % <--- changed
        row{1} = {font=\bfseries, c, m},
        row{2} = {cmd=\rotcell},
        row{odd [3-Y]} = {bg=bgodd},                % <--- changed
        }
        \toprule
        \SetRow{bg=white}
        Paper(s) &
        \SetCell[c=3]{c, m} {Data\\ Sources} &&&
            \SetCell[c=10]{c, m} Technique &&&&&&&&& \\
        \midrule
        & Log-based
            & Distributed Tracing-based
            & Monitoring-Based
            & {Unsupervised\\ learning}
            & {Supervised\\ learning}
            & Reinforcement learning
            & Semi-supervised learning
            & Hybrid learning
            & {Statistical\\ Approach}
            & Causal Inference
            & {Trace\\ comparison}
            & Heart Beating
            & SLO checks \\
        \midrule
        \cite{liu2020unsupervised, pahl2018all, jin2020anomaly, bogatinovski2020self} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        \textbullet &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
           % SLO checks 
        \\
        \cite{nedelkoski2019anomaly, gan2019leveraging, zhou2019latent} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        \textbullet &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{wang2020workflow, chen2020framework, meng2021detecting} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        \textbullet &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{li2021microservice} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        \textbullet &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{chow2014mystery} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        \textbullet &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{belhadi2021reinforcement} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        \textbullet &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{sharma2013cloudpd, zhang2016taskinsight, xu2018unsupervised, gulenko2018detecting, mariani2018localizing, wu2020microrca, wu2020performance, wang2018cloudranger, vallis2014novel, su2019robust, huang2013lof, bhaduri2011detecting,wang2012workload, lazarevic2003comparative} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        \textbullet &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{sauvanaud2018anomaly, liu2015opprentice, du2018anomaly, mariani2020predicting} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        \textbullet &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{magalhaes2010detection, peiris2014pad, abdelrahman2016detection, kang2012dapa, yang2007anomaly, wang2013energy, ahad2015toward, nguyen2013fchain, tan2012prepare, gu2009online, samir2019dla} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        \textbullet &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{wu2021causal, chen2014causeinfer, chen2016causeinfer, lin2018microscope} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        \textbullet &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks 
        \\
        \cite{shan2019diagnosis} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        \textbullet  % SLO checks 
        \\
        \cite{zang2018fault} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        \textbullet  &   % HeartBeating
        % SLO checks 
        \\
        \cite{yagoub2018equipment, brown2018recurrent, nandi2016anomaly, jia2017logsed, fu2009execution, du2017deeplog} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        \textbullet &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks 
        \\
        \cite{fronza2013failure, zhang2016automated, zhang2019robust, liang2007failure} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        \textbullet &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks 
        \\
        \cite{meng2019loganomaly} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        \textbullet &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks 
        \\
       \cite{meng2019loganomaly, yang2021semi, li2021microservice} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        \textbullet &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks 
        \\
        \cite{salfner2007using, beschastnikh2014inferring} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        \textbullet &  % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks 
        \\
        \cite{he2020loghub} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        \textbullet &   % Hybrid learning
        &  % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks 
        \\
        \midrule
    \end{tblr}  
    {\label{tab:tbl_results}}
\end{sidewaystable*}

\clearpage
\newpage

bla bla

\end{document}
7
  • 2
    Please provide MWE i(small but complete document with code of of your table) n your question. Given link may disappear after while ...
    – Zarko
    Nov 1 at 17:38
  • BTW, file with table code is not available to us.
    – Zarko
    Nov 1 at 17:44
  • Code added. Thanks for the heads-up. Nov 1 at 18:18
  • hm, your table is very unusual. You (individually) rotate all cells, Is an option to put table in landscape page?
    – Zarko
    Nov 1 at 19:59
  • No, the page has to be in a vertical orientation (portrait) and so does the table. Then use the rotatebox command. Nov 1 at 20:44

1 Answer 1

1

Something like this?

enter image description here

  • table is put in landscape page, so width of table is equal to \textheight
  • in this case table caption is aligned with table, what is to my opinion correct position of caption

MWE:

\documentclass{article}
\usepackage{caption}
%------ tables
\usepackage{rotating}
\usepackage{makecell}
\usepackage{xcolor}
    \colorlet{bgodd}{black!10}
\usepackage{tabularray}
     \UseTblrLibrary{booktabs}
\usepackage{pdflscape}
%------

\begin{document}
\begin{landscape}
    \begin{table}[ht]
\caption{Test}
\label{tab:tbl_results}
\settowidth\rotheadsize{\small Monitoring-Based}    % from makecell
    \begin{tblr}{
        colspec = {l *{13}{X[c]}},
        vline{2-Y} = {2-Z}{dotted},
        vline{2,5} = {1-Z}{solid, \lightrulewidth},  % \lightrulewidth is defined in booktabs
        colsep = 2pt,
        row{1} = {font=\bfseries, c, m},
        row{2} = {cmd=\rotcell},
        row{odd [3-Y]} = {bg=bgodd},                 % <--- changed
       \toprule
        Paper(s) &
        \SetCell[c=3]{c, m} {Data\\ Sources} &&&
            \SetCell[c=10]{c, m} Technique &&&&&&&&& \\
        \midrule
        & Log-based
            & Distributed Tracing-based
            & Monitoring-Based
            & {Unsupervised\\ learning}
            & {Supervised\\ learning}
            & Reinforcement learning
            & Semi-supervised learning
            & Hybrid learning
            & {Statistical\\ Approach}
            & Causal Inference
            & {Trace\\ comparison}
            & Heart Beating
            & SLO checks \\
        \midrule
        \cite{liu2020unsupervised, pahl2018all, jin2020anomaly, bogatinovski2020self} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        \textbullet &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
           % SLO checks
        \\
        \cite{nedelkoski2019anomaly, gan2019leveraging, zhou2019latent} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        \textbullet &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{wang2020workflow, chen2020framework, meng2021detecting} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        \textbullet &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{li2021microservice} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        \textbullet &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{chow2014mystery} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        \textbullet &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{belhadi2021reinforcement} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        \textbullet &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{sharma2013cloudpd, zhang2016taskinsight, xu2018unsupervised, gulenko2018detecting, mariani2018localizing, wu2020microrca, wu2020performance, wang2018cloudranger, vallis2014novel, su2019robust, huang2013lof, bhaduri2011detecting,wang2012workload, lazarevic2003comparative} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        \textbullet &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{sauvanaud2018anomaly, liu2015opprentice, du2018anomaly, mariani2020predicting} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        \textbullet &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{magalhaes2010detection, peiris2014pad, abdelrahman2016detection, kang2012dapa, yang2007anomaly, wang2013energy, ahad2015toward, nguyen2013fchain, tan2012prepare, gu2009online, samir2019dla} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        \textbullet &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{wu2021causal, chen2014causeinfer, chen2016causeinfer, lin2018microscope} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        \textbullet &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{shan2019diagnosis} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        \textbullet  % SLO checks
        \\
        \cite{zang2018fault} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        \textbullet  &   % HeartBeating
        % SLO checks
        \\
        \cite{yagoub2018equipment, brown2018recurrent, nandi2016anomaly, jia2017logsed, fu2009execution, du2017deeplog} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        \textbullet &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks
        \\
        \cite{fronza2013failure, zhang2016automated, zhang2019robust, liang2007failure} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        \textbullet &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks
        \\
        \cite{meng2019loganomaly} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        \textbullet &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks
        \\
       \cite{meng2019loganomaly, yang2021semi, li2021microservice} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        \textbullet &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks
        \\
        \cite{salfner2007using, beschastnikh2014inferring} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        \textbullet &  % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks
        \\
        \cite{he2020loghub} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        \textbullet &   % Hybrid learning
        &  % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks
        \\
        \bottomrule
    \end{tblr}
    \end{table}
\end{landscape}
\end{document}

Addendum (1):

  • in the case, that you like rotate only table and left its caption at top of page, you should define width of table
  • in this case it is about \textwidth - space for caption (see MWE below):
\documentclass{article}
\usepackage{caption}
%------ tables
\usepackage{rotating}
\usepackage{makecell}
\usepackage{xcolor}
    \colorlet{bgodd}{black!10}
\usepackage{tabularray}
     \UseTblrLibrary{booktabs}
%------

\begin{document}
    \begin{table}[ht]
\caption{Test}
\label{tab:tbl_results}
\settowidth\rotheadsize{\small Monitoring-Based}    % from makecell
 \rotatebox{90}{
    \begin{tblr}{width=\textheight-3\baselineskip,  % <--- added
        colspec = {l *{13}{X[c]}},
        vline{2-Y} = {2-Z}{dotted},
        vline{2,5} = {1-Z}{solid, \lightrulewidth}, % \lightrulewidth is defined in booktabs
        colsep = 1pt,                               % <--- changed
        row{1} = {font=\bfseries, c, m},
        row{2} = {cmd=\rotcell},
        row{odd [3-Y]} = {bg=bgodd},                % <--- changed
                }
        \toprule
Paper(s) &  \SetCell[c=3]{c} {Data\\ Sources} 
            &&& \SetCell[c=10]{c, m} Technique 
                &&&&&&&&&       \\
        \midrule
        & Log-based
            & Distributed Tracing-based
            & Monitoring-Based
            & {Unsupervised\\ learning}
            & {Supervised\\ learning}
            & Reinforcement learning
            & Semi-supervised learning
            & Hybrid learning
            & {Statistical\\ Approach}
            & Causal Inference
            & {Trace\\ comparison}
            & Heart Beating
            & SLO checks \\
        \midrule
        \cite{liu2020unsupervised, pahl2018all, jin2020anomaly, bogatinovski2020self} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        \textbullet &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
           % SLO checks
        \\
        \cite{nedelkoski2019anomaly, gan2019leveraging, zhou2019latent} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        \textbullet &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{wang2020workflow, chen2020framework, meng2021detecting} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        \textbullet &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{li2021microservice} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        \textbullet &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{chow2014mystery} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        \textbullet &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{belhadi2021reinforcement} &
        &  % Log-based
        \textbullet &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        \textbullet &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{sharma2013cloudpd, zhang2016taskinsight, xu2018unsupervised, gulenko2018detecting, mariani2018localizing, wu2020microrca, wu2020performance, wang2018cloudranger, vallis2014novel, su2019robust, huang2013lof, bhaduri2011detecting,wang2012workload, lazarevic2003comparative} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        \textbullet &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{sauvanaud2018anomaly, liu2015opprentice, du2018anomaly, mariani2020predicting} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        \textbullet &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{magalhaes2010detection, peiris2014pad, abdelrahman2016detection, kang2012dapa, yang2007anomaly, wang2013energy, ahad2015toward, nguyen2013fchain, tan2012prepare, gu2009online, samir2019dla} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        \textbullet &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{wu2021causal, chen2014causeinfer, chen2016causeinfer, lin2018microscope} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        \textbullet &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
         % SLO checks
        \\
        \cite{shan2019diagnosis} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        \textbullet  % SLO checks
        \\
        \cite{zang2018fault} &
        &  % Log-based
        &   % Distributed Tracing-based
        \textbullet &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        \textbullet  &   % HeartBeating
        % SLO checks
        \\
        \cite{yagoub2018equipment, brown2018recurrent, nandi2016anomaly, jia2017logsed, fu2009execution, du2017deeplog} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        \textbullet &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks
        \\
        \cite{fronza2013failure, zhang2016automated, zhang2019robust, liang2007failure} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        \textbullet &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks
        \\
        \cite{meng2019loganomaly} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        \textbullet &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks
        \\
       \cite{meng2019loganomaly, yang2021semi, li2021microservice} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        \textbullet &   % Semi-supervised learning
        &   % Hybrid learning
        &   % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks
        \\
        \cite{salfner2007using, beschastnikh2014inferring} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        &   % Hybrid learning
        \textbullet &  % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks
        \\
        \cite{he2020loghub} &
        \textbullet &  % Log-based
        &   % Distributed Tracing-based
        &   % Monitoring-Based
        &   % Unsupervised learning
        &   % Supervised learning
        &   % Reinforcement learning
        &   % Semi-supervised learning
        \textbullet &   % Hybrid learning
        &  % Statistical Approach
        &   % Causal Inference
        &   % Trace comparison
        &   % HeartBeating
        % SLO checks
        \\
        \bottomrule
    \end{tblr}
}
    \end{table}
\end{document}

enter image description here

Addendum (2):

  • to be pedentic, also the second solution has changed orientation of table
  • for truly portrait oriented table it had not be oriented, but should be reduced width of the first column and maybe also reduced linespread of text in other columns. For example as is done in the next MWE:
\documentclass{article}
\usepackage{caption}
%------ tables
\usepackage{rotating}
\usepackage{makecell}
\usepackage{xcolor}
    \colorlet{bgodd}{black!10}
\usepackage{tabularray}
     \UseTblrLibrary{booktabs}
%------

\begin{document}
    \begin{table}[ht]
\caption{Test}
\label{tab:tbl_results}
\small
\settowidth\rotheadsize{Monitoring-Based}    % from makecell
    \begin{tblr}{
        colspec = {X[2,l,m] *{13}{X[c, font=\linespread{0.84}\selectfont]}},
        vline{2-Y} = {2-Z}{dotted},
        vline{2,5} = {1-Z}{solid, \lightrulewidth}, % \lightrulewidth is defined in booktabs
        colsep = 1pt,                               % <--- changed
        row{1} = {font=\bfseries, c, m},
        row{2} = {cmd=\rotcell},
        row{odd [3-Y]} = {bg=bgodd},                % <--- changed
                }
%%% table body is the same as at previous examples
        \bottomrule
    \end{tblr}
    \end{table}
\end{document}

enter image description here

3
  • However, if you can write references in more concise way (not listed each separately), you can have table in in portrait orientation (what is requested, if I correctly understood your comment).
    – Zarko
    Nov 1 at 21:10
  • Thanks a lot for the help @Zarko! I ended up choosing to use "sidewaystable" instead of "rotate" so that the caption is vertical. I'm not sure how I can make the references more concise. The solution found is already at an acceptable level. Nov 2 at 10:28
  • @user2989745, somepackages convert \cite{bib1, bib2, bib3, bib4, bib5} to [1-5]
    – Zarko
    Nov 2 at 10:31

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