文章中的表格边距

Question

我有下表：

我有以下问题：

1. 有什么方法可以强制表格与文章文本的宽度相同？
2. 并且强制所有列具有相同的大小？
3. 另外，强制表格只出现在一页上，而不与页码重叠？

代码：

\newcolumntype{C}{>{\centering\arraybackslash}X}
\newcolumntype{P}[1]{>{\centering\arraybackslash}p{#1}}
\newcommand{\tilt}[2][14em]{\rotatebox[origin=r]{90}{\parbox{#1}{\raggedleft #2}}}
\noindent\begin{table}
\FloatBarrier
\renewcommand{\arraystretch}{1.2}%
\begin{tabularx}{\textwidth}{ |P{12em}| |C|C|C| |C|C|C|C|C|C|C|C|C|C| }

    \hline
    \textbf{Paper(s)} &
    \multicolumn{3}{|c||}{\textbf{Data Sources}} &
    \multicolumn{9}{|c||}{\textbf{Technique}} \\

    \cline{2-13}
    &
    \tilt{\textbf{Log-based}} &
    \tilt{\textbf{Distributed Tracing-based}} &
    \tilt{\textbf{Monitoring-Based}} &
    \tilt{\textbf{Unsupervised learning}} &
    \tilt{\textbf{Supervised learning}} &
    \tilt{\textbf{Reinforcement learning}} &
    \tilt{\textbf{Semi-supervised learning}} &
    \tilt{\textbf{Statistical Approach}} &
    \tilt{\textbf{Causal Inference}} &
    \tilt{\textbf{Trace comparison}} &
    \tilt{\textbf{HeartBeating}} &
    \tilt{\textbf{SLO checks}} \\

    \hline
    \cite{liu2020unsupervised, nedelkoski2019anomaly, vallis2014novel, pahl2018all, jin2020anomaly, bogatinovski2020self} &
    &  % Log-based
    \textbullet &   % Distributed Tracing-based
    &   % Monitoring-Based
    \textbullet &   % Unsupervised learning
    &   % Supervised learning
    &   % Reinforcement learning
    &   % Semi-supervised learning
    &   % Statistical Approach
    &   % Causal Inference
    &   % Trace comparison
    &   % HeartBeating
       % SLO checks 
    
    \\  
    \hline

    \cite{gan2019leveraging, zhou2019latent} &
    &  % Log-based
    \textbullet &   % Distributed Tracing-based
    &   % Monitoring-Based
    &   % Unsupervised learning
    \textbullet &   % Supervised learning
    &   % Reinforcement learning
    &   % Semi-supervised learning
    &   % Statistical Approach
    &   % Causal Inference
    &   % Trace comparison
    &   % HeartBeating
     % SLO checks 
    \\  
    \hline

    \cite{wang2020workflow, chen2020framework, meng2021detecting} &
    &  % Log-based
    \textbullet &   % Distributed Tracing-based
    &   % Monitoring-Based
    &   % Unsupervised learning
    &   % Supervised learning
    &   % Reinforcement learning
    &   % Semi-supervised learning
    &   % Statistical Approach
    &   % Causal Inference
    \textbullet &   % Trace comparison
    &   % HeartBeating
     % SLO checks 
    \\  
    \hline
    
\end{tabularx}
\FloatBarrier
\end{table}

---

更新

我按照@Zarko 的建议编辑了表格。是否可以降低文章行高？我觉得高度太大，导致表格超出了页面边缘。

代码：

\documentclass{article}
\usepackage{geometry}
%--------------- show page layout. don't use in a real document!
\usepackage{showframe}
\renewcommand\ShowFrameLinethickness{0.15pt}
\renewcommand*\ShowFrameColor{\color{red}}
%
\usepackage{lipsum}                             % for dummy text
%---------------------------------------------------------------%
\usepackage{rotating}
\usepackage{makecell}
\usepackage{tabularray}


\begin{document}
    \begin{table}[ht]
\settowidth\rotheadsize{\small Monitoring-Based}    % from makecell
\begin{tblr}{hlines, vlines,
             colspec = {Q[l, wd=11em] | *{3}{X[c]} | *{9}{X[c]}},
             colsep = 3pt,
             row{1} = {font=\small\bfseries\linespread{0,84}\selectfont, c, m},
             row{2} = {cmd=\rotcell, font=\small\linespread{0,84}\selectfont, rowsep=0pt}
            }
Paper(s)
    &   \SetCell[c=3]{c, m}    {Data\\ Sources}
        &   &   &   \SetCell[c=9]{c, m}   Technique
                    &   &   &   &   &   &   &   &                  \\
    &   Log-based
        &   Distributed Tracing-based
            &   Monitoring-Based
                &   {Unsupervised\\ learning}
                    &   Supervised learning
                        &   Reinforcement learning
                            &   Semi-supervised learning
                                &   Statistical Approach
                                    &   Causal Inference
                                        &   Trace comparison
                                            &   Heart Beating
                                                &   SLO checks  \\
    
    \cite{liu2020unsupervised, nedelkoski2019anomaly, vallis2014novel, pahl2018all, jin2020anomaly, bogatinovski2020self} &
    &  % Log-based
    \textbullet &   % Distributed Tracing-based
    &   % Monitoring-Based
    \textbullet &   % Unsupervised learning
    &   % Supervised learning
    &   % Reinforcement learning
    &   % Semi-supervised learning
    &   % Statistical Approach
    &   % Causal Inference
    &   % Trace comparison
    &   % HeartBeating
       % SLO checks 
    
    \\  
    

    \cite{gan2019leveraging, zhou2019latent} &
    &  % Log-based
    \textbullet &   % Distributed Tracing-based
    &   % Monitoring-Based
    &   % Unsupervised learning
    \textbullet &   % Supervised learning
    &   % Reinforcement learning
    &   % Semi-supervised 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
    &   % 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
    &   % 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
    &   % 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
    &   % Statistical Approach
    &   % Causal Inference
    &   % Trace comparison
    &   % HeartBeating
     % SLO checks 
    \\  
    
    
    \cite{sharma2013cloudpd, zhang2016taskinsight, xu2018unsupervised, gulenko2018detecting, mariani2018localizing, wu2020microrca, wu2020performance, wang2018cloudranger} &
    &  % Log-based
    &   % Distributed Tracing-based
    \textbullet &   % Monitoring-Based
    \textbullet &   % Unsupervised learning
    &   % Supervised learning
    &   % Reinforcement learning
    &   % Semi-supervised learning
    &   % Statistical Approach
    &   % Causal Inference
    &   % Trace comparison
    &   % HeartBeating
     % SLO checks 
    \\  
    
    
    \cite{sauvanaud2018anomaly, liu2015opprentice, du2018anomaly, mariani2020predicting, samir2019dla} &
    &  % Log-based
    &   % Distributed Tracing-based
    \textbullet &   % Monitoring-Based
    &   % Unsupervised learning
    \textbullet &   % Supervised learning
    &   % Reinforcement learning
    &   % Semi-supervised learning
    &   % Statistical Approach
    &   % Causal Inference
    &   % Trace comparison
    &   % HeartBeating
     % SLO checks 
    \\  
    
    
    \cite{magalhaes2010detection, peiris2014pad, abdelrahman2016detection, kang2012dapa, yang2007anomaly, wang2013energy, ahad2015toward, nguyen2013fchain, tan2012prepare, gu2009online} &
    &  % Log-based
    &   % Distributed Tracing-based
    \textbullet &   % Monitoring-Based
    &   % Unsupervised learning
    &   % Supervised learning
    &   % Reinforcement learning
    &   % Semi-supervised learning
    \textbullet &   % Statistical Approach
    &   % Causal Inference
    &   % Trace comparison
    &   % HeartBeating
     % SLO checks 
    \\  
    
    
    \cite{chen2014causeinfer, chen2016causeinfer, shan2019diagnosis, lin2018microscope} &
    &  % Log-based
    &   % Distributed Tracing-based
    \textbullet &   % Monitoring-Based
    &   % Unsupervised learning
    &   % Supervised learning
    &   % Reinforcement learning
    &   % Semi-supervised 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
    &   % Statistical Approach
    &   % Causal Inference
    &   % Trace comparison
    \textbullet  &   % HeartBeating
    % SLO checks 
    \\  
    
    
    \cite{du2017deeplog, yagoub2018equipment, liang2007failure, zhang2016automated, brown2018recurrent, meng2019loganomaly, zhang2019robust, nandi2016anomaly, jia2017logsed, fu2009execution} &
    \textbullet &  % Log-based
    &   % Distributed Tracing-based
    &   % Monitoring-Based
    \textbullet &   % Unsupervised learning
    &   % Supervised learning
    &   % Reinforcement learning
    &   % Semi-supervised learning
    &   % Statistical Approach
    &   % Causal Inference
    &   % Trace comparison
    &   % HeartBeating
    % SLO checks 
    \\  
    

    \cite{fronza2013failure} &
    \textbullet &  % Log-based
    &   % Distributed Tracing-based
    &   % Monitoring-Based
    &   % Unsupervised learning
    \textbullet &   % Supervised learning
    &   % Reinforcement learning
    &   % Semi-supervised learning
    &   % Statistical Approach
    &   % Causal Inference
    &   % Trace comparison
    &   % HeartBeating
    % SLO checks 
    \\  
    
    
    \cite{fu2009execution} &
    \textbullet &  % Log-based
    &   % Distributed Tracing-based
    &   % Monitoring-Based
    \textbullet &   % Unsupervised learning
    \textbullet &   % Supervised learning
    &   % Reinforcement learning
    &   % Semi-supervised learning
    &   % Statistical Approach
    &   % Causal Inference
    &   % Trace comparison
    &   % HeartBeating
    % SLO checks 
    \\  
    

   \cite{yang2021semi} &
    \textbullet &  % Log-based
    &   % Distributed Tracing-based
    &   % Monitoring-Based
    &   % Unsupervised learning
    &   % Supervised learning
    &   % Reinforcement learning
    \textbullet &   % Semi-supervised 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
    \textbullet &   % Statistical Approach
    &   % Causal Inference
    &   % Trace comparison
    &   % HeartBeating
    % SLO checks 
    \\  
    
\end{tblr}
    \end{table}
\end{document}

谢谢

Answer 1

使用tabularray、rotating和makecell包后，旋转的文本在必要时会分为两行。

编辑： 表格代码已扩展，其中包含编辑问题中提供的行。添加行后，表格仍可放在一页上。

\documentclass{article}
\usepackage{geometry}
%--------------- show page layout. don't use in a real document!
\usepackage{showframe}
\renewcommand\ShowFrameLinethickness{0.15pt}
\renewcommand*\ShowFrameColor{\color{red}}
%
\usepackage{lipsum}                             % for dummy text
%---------------------------------------------------------------%
\usepackage{rotating}
\usepackage{makecell}
\usepackage{tabularray}


\begin{document}
    \begin{table}[ht]
\settowidth\rotheadsize{\small Monitoring-Based}    % from makecell
\begin{tblr}{hlines, vlines,
             colspec = {Q[l, m, wd=11em] | *{3}{X[c]} | *{9}{X[c]}},
             colsep = 3pt,
             row{1} = {font=\small\bfseries\linespread{0,84}\selectfont, c, m},
             row{2} = {cmd=\rotcell, font=\small\linespread{0,84}\selectfont, rowsep=0pt}
            }
Paper(s)
    &   \SetCell[c=3]{c, m}    {Data\\ Sources}
        &   &   &   \SetCell[c=9]{c, m}   Technique
                    &   &   &   &   &   &   &   &                  \\
    &   Log-based
        &   Distributed Tracing-based
            &   Monitoring-Based
                &   {Unsupervised\\ learning}
                    &   Supervised learning
                        &   Reinforcement learning
                            &   Semi-supervised learning
                                &   Statistical Approach
                                    &   Causal Inference
                                        &   Trace comparison
                                            &   Heart Beating
                                                &   SLO checks  \\
\cite{liu2020unsupervised, nedelkoski2019anomaly, vallis2014novel, pahl2018all, jin2020anomaly, bogatinovski2020self}
    & \textbullet
        &   &   & \textbullet
                    &   &   &   &   &   &   &   &               \\
\cite{gan2019leveraging, zhou2019latent} 
    &   & \textbullet
            &   & \textbullet
                    &   &   &   &   &   &   &   &               \\
\cite{wang2020workflow, chen2020framework, meng2021detecting}
    &   & \textbullet
            &   &   &   &   &   &   &   & \textbullet
                                            &   &               \\
% data from added rows in question
\cite{li2021microservice} &
    &  \textbullet 
        &   &   &   &   & \textbullet 
                            &   &   &   &   &   &               \\
\cite{chow2014mystery} 
    &   & \textbullet 
            &   &   &   &   &   &   & \textbullet 
                                        &   &   &               \\
\cite{belhadi2021reinforcement} 
    &   & \textbullet 
            &   &   &   & \textbullet 
                            &   &   &   &   &   &               \\
\cite{sharma2013cloudpd, zhang2016taskinsight, xu2018unsupervised, gulenko2018detecting, mariani2018localizing, wu2020microrca, wu2020performance, wang2018cloudranger} 
    &   &   & \textbullet 
                & \textbullet 
                    &   &   &   &   &   &   &   &               \\
\cite{sauvanaud2018anomaly, liu2015opprentice, du2018anomaly, mariani2020predicting, samir2019dla}
    &   &   & \textbullet 
                &   & \textbullet 
                        &   &   &   &   &   &   &               \\
\cite{magalhaes2010detection, peiris2014pad, abdelrahman2016detection, kang2012dapa, yang2007anomaly, wang2013energy, ahad2015toward, nguyen2013fchain, tan2012prepare, gu2009online} 
    &   &   & \textbullet
                &   &   &   &   & \textbullet 
                                    &   &   &   &               \\
\cite{chen2014causeinfer, chen2016causeinfer, shan2019diagnosis, lin2018microscope} 
    &   &   & \textbullet 
                &   &   &   &   &   &   &   &   & \textbullet   \\
\cite{zang2018fault} 
    &   &   & \textbullet 
                &   &   &   &   &   &   &   & \textbullet  
                                                &               \\
\cite{du2017deeplog, yagoub2018equipment, liang2007failure, zhang2016automated, brown2018recurrent, meng2019loganomaly, zhang2019robust, nandi2016anomaly, jia2017logsed, fu2009execution} 
    & \textbullet 
        &   &   & \textbullet 
                    &   &   &   &   &   &   &   &               \\
\cite{fronza2013failure} 
    & \textbullet 
        &   &   &   & \textbullet 
                        &   &   &   &   &   &   &               \\
\cite{fu2009execution} 
    & \textbullet 
        &   &   & \textbullet 
                    & \textbullet 
                        &   &   &   &   &   &   &               \\
\cite{yang2021semi} 
    & \textbullet 
        &   &   &   &   &   & \textbullet 
                                &   &   &   &   &               \\
\cite{salfner2007using, beschastnikh2014inferring} 
    & \textbullet 
        &   &   &   &   &   &   & \textbullet 
                                    &   &   &   &               \\

\end{tblr}
    \end{table}
\end{document}

Paper(s)    
    &   \SetCell[c=3]{c, m}    {Data\\ Sources}
        &   &   &   \SetCell[c=9]{c, m}   Technique                
                    &   &   &   &   &   &   &   &   &              \\
\end{tblr}
    \end{table}
\end{document}

（红线表示页面布局）

笔记：

• 除第一列外，所有列的宽度均相等。
• 表格宽度等于\textwidth。
• 表格可以轻松放入一页，甚至在编辑的问题中添加宽度行
• 我们没有关于您的文档页面布局的任何信息，因此不知道有多少空间可用于表格。现在在编辑的问题中被视为 MWE（显然是基于此答案）。
• 请在您最终的新问题中始终提供 MWE ) 最小工作示例 =，一份完整的小文档，其中显示了您的问题

Answer 2

除了 Zarko 的回答之外，您还可以选择减少数字行以及使用自定义规则booktabs。桌子很大。一个建议是在横向环境中排版表格。另一种方法是添加交替颜色并删除多条水平线，如果您想增加每行的高度，这可能更可取。

\documentclass{article}
% \usepackage{geometry}   % for changing a document layout
\usepackage{pdflscape}
\usepackage{rotating}
\usepackage{makecell}
\usepackage{xcolor}
    \colorlet{bgodd}{black!10}
\usepackage{tabularray}
    \UseTblrLibrary{booktabs}
\usepackage{lipsum}       % for dummy text


\begin{document}
\lipsum[1]

\begin{landscape}
    \begin{table}[ht]
        % from makecell
        \settowidth\rotheadsize{\small Monitoring-Based}
        \begin{tblr}{
                colspec = {Q[l, wd=5cm] *{3}{X[c]} *{9}{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=9]{c, m} Technique &&&&&&&& \\
            \midrule
            & Log-based
                & Distributed Tracing-based
                & Monitoring-Based
                & {Unsupervised\\ learning}
                & Supervised learning
                & Reinforcement learning
                & Semi-supervised learning
                & Statistical Approach
                & Causal Inference
                & Trace comparison
                & Heart Beating
                & SLO checks \\
            \midrule
            \cite{liu2020unsupervised, nedelkoski2019anomaly, vallis2014novel, pahl2018all, jin2020anomaly, bogatinovski2020self} &
            &  % Log-based
            \textbullet &   % Distributed Tracing-based
            &   % Monitoring-Based
            \textbullet &   % Unsupervised learning
            &   % Supervised learning
            &   % Reinforcement learning
            &   % Semi-supervised learning
            &   % Statistical Approach
            &   % Causal Inference
            &   % Trace comparison
            &   % HeartBeating
               % SLO checks 
            \\
            \cite{gan2019leveraging, zhou2019latent} &
            &  % Log-based
            \textbullet &   % Distributed Tracing-based
            &   % Monitoring-Based
            &   % Unsupervised learning
            \textbullet &   % Supervised learning
            &   % Reinforcement learning
            &   % Semi-supervised 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
            &   % 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
            &   % 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
            &   % 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
            &   % Statistical Approach
            &   % Causal Inference
            &   % Trace comparison
            &   % HeartBeating
             % SLO checks 
            \\
            \cite{sharma2013cloudpd, zhang2016taskinsight, xu2018unsupervised, gulenko2018detecting, mariani2018localizing, wu2020microrca, wu2020performance, wang2018cloudranger} &
            &  % Log-based
            &   % Distributed Tracing-based
            \textbullet &   % Monitoring-Based
            \textbullet &   % Unsupervised learning
            &   % Supervised learning
            &   % Reinforcement learning
            &   % Semi-supervised learning
            &   % Statistical Approach
            &   % Causal Inference
            &   % Trace comparison
            &   % HeartBeating
             % SLO checks 
            \\
            \cite{sauvanaud2018anomaly, liu2015opprentice, du2018anomaly, mariani2020predicting, samir2019dla} &
            &  % Log-based
            &   % Distributed Tracing-based
            \textbullet &   % Monitoring-Based
            &   % Unsupervised learning
            \textbullet &   % Supervised learning
            &   % Reinforcement learning
            &   % Semi-supervised learning
            &   % Statistical Approach
            &   % Causal Inference
            &   % Trace comparison
            &   % HeartBeating
             % SLO checks 
            \\
            \cite{magalhaes2010detection, peiris2014pad, abdelrahman2016detection, kang2012dapa, yang2007anomaly, wang2013energy, ahad2015toward, nguyen2013fchain, tan2012prepare, gu2009online} &
            &  % Log-based
            &   % Distributed Tracing-based
            \textbullet &   % Monitoring-Based
            &   % Unsupervised learning
            &   % Supervised learning
            &   % Reinforcement learning
            &   % Semi-supervised learning
            \textbullet &   % Statistical Approach
            &   % Causal Inference
            &   % Trace comparison
            &   % HeartBeating
             % SLO checks 
            \\
            \cite{chen2014causeinfer, chen2016causeinfer, shan2019diagnosis, lin2018microscope} &
            &  % Log-based
            &   % Distributed Tracing-based
            \textbullet &   % Monitoring-Based
            &   % Unsupervised learning
            &   % Supervised learning
            &   % Reinforcement learning
            &   % Semi-supervised 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
            &   % Statistical Approach
            &   % Causal Inference
            &   % Trace comparison
            \textbullet  &   % HeartBeating
            % SLO checks 
            \\
            \cite{du2017deeplog, yagoub2018equipment, liang2007failure, zhang2016automated, brown2018recurrent, meng2019loganomaly, zhang2019robust, nandi2016anomaly, jia2017logsed, fu2009execution} &
            \textbullet &  % Log-based
            &   % Distributed Tracing-based
            &   % Monitoring-Based
            \textbullet &   % Unsupervised learning
            &   % Supervised learning
            &   % Reinforcement learning
            &   % Semi-supervised learning
            &   % Statistical Approach
            &   % Causal Inference
            &   % Trace comparison
            &   % HeartBeating
            % SLO checks 
            \\
            \cite{fronza2013failure} &
            \textbullet &  % Log-based
            &   % Distributed Tracing-based
            &   % Monitoring-Based
            &   % Unsupervised learning
            \textbullet &   % Supervised learning
            &   % Reinforcement learning
            &   % Semi-supervised learning
            &   % Statistical Approach
            &   % Causal Inference
            &   % Trace comparison
            &   % HeartBeating
            % SLO checks 
            \\
            \cite{fu2009execution} &
            \textbullet &  % Log-based
            &   % Distributed Tracing-based
            &   % Monitoring-Based
            \textbullet &   % Unsupervised learning
            \textbullet &   % Supervised learning
            &   % Reinforcement learning
            &   % Semi-supervised learning
            &   % Statistical Approach
            &   % Causal Inference
            &   % Trace comparison
            &   % HeartBeating
            % SLO checks 
            \\
           \cite{yang2021semi} &
            \textbullet &  % Log-based
            &   % Distributed Tracing-based
            &   % Monitoring-Based
            &   % Unsupervised learning
            &   % Supervised learning
            &   % Reinforcement learning
            \textbullet &   % Semi-supervised 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
            \textbullet &   % Statistical Approach
            &   % Causal Inference
            &   % Trace comparison
            &   % HeartBeating
            % SLO checks 
            \\
            \midrule
        \end{tblr}
    \end{table}
\end{landscape}

\lipsum[2]
\end{document}