In this section, we will prove that computing $\expct\limits_{\vct{W}\sim\pd}\pbox{\poly(\vct{W})}$ exactly for a \ti-lineage polynomial $\poly(\vct{X})$ generated from a project-join query (even an expression tree representation) is \sharpwonehard. Note that this implies hardness for \bis and general $\semNX$-PDBs under bag semantics. Furthermore, we demonstrate in \Cref{sec:single-p} that the problem remains hard, even if $\probOf[X_i=1]=\prob$ for all $X_i$ and any fixed valued $\prob\in(0, 1)$ as long as certain popular hardness conjectures in fine-grained complexity hold.
Our hardness results are based on (exactly) counting the number of occurrences of a subgraph $H$ in $G$. Let $\numocc{G}{H}$ denote the number of occurrences of $H$ in graph $G$. We can think of $H$ as being of constant size and $G$ as growing. %In query processing, $H$ can be viewed as the query while $G$ as the database instance.
In particular, we will consider the problems of computing the following counts (given $G$ as an input and its adjacency list representation): $\numocc{G}{\tri}$ (the number of triangles), $\numocc{G}{\threedis}$ (the number of $3$-matchings), and the latter's generalization $\numocc{G}{\kmatch}$ (the number of $k$-matchings). Our hardness result in \Cref{sec:multiple-p} is based on the following result:
Given positive integer $k$ and undirected graph $G$ with no self-loops or parallel edges, computing $\numocc{G}{\kmatch}$ exactly is %counting the number of $k$-matchings in $G$ is
The above result means that we cannot hope to count the number of $k$-matchings in $G=(V,E)$ in time $f(k)\cdot |V|^{c}$ for any function $f$ and constant $c$ independent of $k$. In fact, all known algorithms to solve this problem take time $|V|^{\Omega(k)}$.
There exists a constant $\eps_0>0$ such that given an undirected graph $G=(V,E)$, computing exactly $\numocc{G}{\tri}$ cannot be done in time $o\inparen{|E|^{1+\eps_0}}$.
Based on the so called {\em Triangle detection hypothesis} (cf.~\cite{triang-hard}), which states that detection of whether $G$ has a triangle or not takes time $\Omega\inparen{|E|^{4/3}}$, implies that in Conjecture~\ref{conj:graph} we can take $\eps_0\ge\frac13$.
Both of our hardness results rely on a simple query polynomial encoding of the edges of a graph.
To prove our hardness result, consider a graph $G(V, E)$, where $|E| = m$, $|V| =\numvar$. Our query polynomial has a variable $X_i$ for every $i$ in $[\numvar]$.
Our hardness results only need a \ti instance; We also consider the special case when all the tuple probabilities (probabilities assigned to $X_i$ by $\probAllTup$) are the same value. Note that our hardness results % do not require the general circuit representation and
even hold for the expression trees. %this polynomial can be encoded in an expression tree of size $\Theta(km)$.
where adapting the PDB instance in \Cref{fig:ex-shipping-simp}, relation $OnTime$ has $n$ tuples corresponding to each vertex in $V=[n]$ each with probability $\prob$ and $Route(\text{City}_1, \text{City}_2)$ has tuples corresponding to the edges $E$ (each with probability of $1$).\footnote{Technically, $\poly_{G}^\kElem(\vct{X})$ should have variables corresponding to tuples in $Route$ as well, but since they always are present with probability $1$, we drop those. Our argument also works when all the tuples in $Route$ also are present with probability $\prob$ but to simplify notation we assign probability $1$ to edges.}
Note that this implies that our hard query polynomial can be represented as an expression tree produced by a project-join query with same probability value for each input tuple $\prob_i$. %; our hardness result transfers here as well.
% OK: The following (commented-out) sentence feels a bit misplaced here.
% -- by contrast our approximation algorithm in \Cref{sec:algo} can handle lineage polynomials represented as circuits generated by union of select-project-join (SPJU) queries with potentially distinct $\prob_i$ values. % (i.e. we do not need union or select operator to derive our hardness result).
%\AR{need discussion on the `tightness' of various params. First, this is for degree 6 poly-- while things are easy for say deg 2. Second this is for any fixed p. Finally, we only need project-join queries to get the hardness results. Also need to compare this with the generality of the approx upper bound results.}
Computing $\rpoly_G^\kElem(\prob_i,\dots,\prob_i)$ for arbitrary $G$ and any $(2k+1)$ distinct values $\prob_i$ ($0\le i \le2k$) is \sharpwonehard (parameterization is in $k$).
We will prove the above result by reducing from the problem of computing the number of $k$-matchings in $G$. Given the current best-known algorithm for this counting problem, our results imply that unless the state-of-the-art $k$-matching algorithms are improved, we cannot hope to solve our problem in time better than $\Omega_k\inparen{m^{k/2}}$ where $m=\abs{E}$, which is only quadratically faster than expanding $\poly_{G}^\kElem(\vct{X})$ into its \abbrSMB form and then using \Cref{cor:expct-sop}. The approximation algorithm we present in \Cref{sec:algo} has runtime $O_k\inparen{m}$ for this query. % (since it runs in linear-time on all lineage polynomials).
Let $\prob_0,\ldots, \prob_{2\kElem}$ be distinct values in $(0, 1]$. Then given the values $\rpoly_{G}^\kElem(\prob_i,\ldots, \prob_i)$ for $0\leq i\leq2\kElem$, the number of $\kElem$-matchings in $G$ can be computed in $O\inparen{\kElem^3}$ time.
