\AH{The notation used here is different than in~\Cref{sec:background}, in particular~\Cref{eq:expect-q-nx}. Maybe we should decide on a notation and try to stick to it as much as possible?}
\BG{We sometimes use $\expct_{\vct{X}\sim P}$ sometimes $\expct_{\vct{X}}$}
In this section, we will prove that computing $\expct\limits_{\vct{W}\sim\pd}\pbox{\poly(\vct{W})}$ for a \ti-lineage polynomial $\poly(\vct{X})$ generated from a project-join query is \sharpwonehard. Note that this implies hardness for \bis and general $\semNX$-PDBs. Furthermore, we demonstrate \Cref{sec:single-p} that the problem remains hard, even if $\probOf(X_i)=\prob$ for all $X_i$ and some fixed valued $\prob$ as long as these conjectures hold. Finally, using popular hardness conjectures in fine-grained complexity we show that if these conjectures hold and except for the trivial choices of $\prob\in\{0,1\}$, the problem is hard for any given $\prob$.
% We would like to argue for a compressed version of $\poly(\vct{X})$, in general $\expct\limits_{\vct{X} \sim \pd}\pbox{\poly(\vct{X})}$ even for tis, cannot be computed in linear time. We will argue two flavors of such a hardness result. In Section~\ref{sec:multiple-p}, we argue that computing the expected value exactly for all query polynommials $\poly(\vct{X})$ for multiple values of $p$ is \sharpwonehard. However, this does not rule out the possibility of being able to solve the problem for a any {\em fixed} value of $p$ in linear time. In Section~\ref{sec:single-p}, we rule out even this possibility (based on some popular hardness conjectures in fine-grained complexity).
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), $\numocc{G}{\threepath}$ (the number of $3$-paths), $\numocc{G}{\threedis}$ (the number of $3$-matchings or collection of three node-disjoint edges) and its generalization $\numocc{G}{\kmatch}$ (the number of $k$-matchings or collections of $k$ node-disjoint edges).
Given a positive integer $k$ and an 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 the values $\numocc{G}{\tri}$, $\numocc{G}{\threepath}$ and $\numocc{G}{\threedis}$ 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 by to $X_i$ by $\probAllTup$) are the same value. Note that this polynomial can be encoded in an expression tree of size $\Theta(km)$.
where generalizaing the PDB instance in Example~\ref{ex:intro}, relation $R$ has $n$ tuples corresponding to each vertex in $V=[n]$ each with probability $\prob$ and $E(A,B)$ has tuples corresponding to the edges in $E$ (each with probability of $1$).\footnote{Technically, $\poly_{G}^\kElem(\vct{X})$ should have variables corresponding to tuples in $E$ as well, but since they always are present with probability $1$, we drop those. Our argument also works when all the tuples in $E$ also are present with probability $\prob$ but to simplify notation we assign probability $1$ to edges.}
Note that this imples that our hard query polynomial can be created from a project-join query -- by contrast our approximation algorithm in \Cref{sec:algo} can handle lineage polynomials generated by union of select-project-join (SPJU) queries. % (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 reduction 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 $\Omega_k\inparen{m^{k/2}}$, which is only quadratically faster than expanding $\poly_{G}^\kElem(\vct{X})$ into its \abbrSMB form and then using \Cref{cor:expct-sop}. By contrast 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.
%It is trivial to see that one can readily expand the exponential expression by performing the $n^\kElem$ product operations, yielding the polynomial in the sum of products form of the lemma statement. By definition $\rpoly_{G}^\kElem$ reduces all variable exponents greater than $1$ to $1$. Thus, a monomial such as $X_i^\kElem X_j^\kElem$ is $X_iX_j$ in $\rpoly_{G}^\kElem$, and the value after substitution is $p_i\cdot p_j = p^2$. Further, that the number of terms in the sum is no greater than $2\kElem + 1$, can be easily justified by the fact that each edge has two endpoints, and the most endpoints occur when we have $\kElem$ distinct edges (such a subgraph is also known as a $\kElem$-matching), with non-intersecting points, a case equivalent to $p^{2\kElem}$.
We first argue that $\rpoly_{G}^\kElem(\prob,\ldots, \prob)=\sum\limits_{i =0}^{2\kElem} c_i \cdot\prob^i$. First, since $\poly_G(\vct{X})$ has %$\kElem$ products of monomials of
degree $2$, it follows that $\poly_G^\kElem(\vct{X})$ has degree $2\kElem$.
%We can further write $\poly_{G}^{\kElem}(\vct{X})$ in its expanded SOP form,
%Since each of $(i_1, j_1),\ldots, (i_\kElem, j_\kElem)$ are from $E$, it follows that the set of $\kElem!$ permutations of the $\kElem$ $X_iX_j$ pairs which form the monomial products are of degree $2\kElem$ with the number of distinct variables in an arbitrary monomial $\leq 2\kElem$.
By definition, $\rpoly_{G}^{\kElem}(\vct{X})$ sets every exponent $e > 1$ to $e =1$, which means that $\degree(\rpoly_{G}^\kElem)\le\degree(\poly_G^\kElem)=2k$. Thus, if we think of $\prob$ as a variable, then $\rpoly_{G}^{\kElem}(\prob,\dots,\prob)$ is a univariate polynomial of degree at most $\degree(\rpoly_{G}^\kElem)\le2k$. Thus, we can write
%thereby shrinking the degree a monomial product term in the SOP form of $\poly_{G}^{\kElem}(\vct{X})$ to the exact number of distinct variables the monomial contains. This implies that $\rpoly_{G}^\kElem$ is a polynomial of degree $2\kElem$ and hence $\rpoly_{G}^\kElem(\prob,\ldots, \prob)$ is a polynomial in $\prob$ of degree $2\kElem$. Then it is the case that
\footnote{Since $\rpoly_G^\kElem(\vct{X})$ does not have any monomial with degree $< 2$, it is the case that $c_0= c_1=0$ but for the sake of simplcity we will ignore this observation.}
Given that we then have $2\kElem+1$ distinct values of $\rpoly_{G}^\kElem(\prob,\ldots, \prob)$ for $0\leq i\leq2\kElem$, it follows that
%we then have $2\kElem + 1$ distinct rows of the form $\prob_i^0\ldots\prob_i^{2\kElem}$ which form a matrix $M$.
we have a linear system of the form $\vct{M}\cdot\vct{c}=\vct{b}$ where the $i$th row of $\vct{M}$ is $\inparen{\prob_i^0\ldots\prob_i^{2\kElem}}$, $\vct{c}$ is the coefficient vector $\inparen{c_0,\ldots, c_{2\kElem}}$, and $\vct{b}$ is the vector such that $\vct{b}[i]=\rpoly_{G}^\kElem(\prob_i,\ldots, \prob_i)$.
In other words, matrix $\vct{M}$ is the Vandermonde matrix, from which it follows that we have a matrix with full rank (the $p_i$'s are distinct), and we can solve the linear system in $O(k^3)$ time (e.g., using Gaussian Elimination) to determine $\vct{c}$ exactly.
Indeed, note that each $\kElem$-matching $(i_1, j_1)\ldots$$(i_k, j_k)$ in $G$ corresponds to the monomial $\prod_{\ell=1}^\kElem X_{i_\ell}X_{j_\ell}$ in $\poly_{G}^\kElem(\vct{X})$, with distinct indexes. %Since each index is distinct, then each variable has an exponent $e = 1$ and this monomial survives in $\rpoly_{G}^{\kElem}(\vct{X})$ Since $\rpoly$ contains only exponents $e \leq 1$, the only degree $2\kElem$ terms that can exist in $\rpoly_{G}^\kElem$ are $\kElem$-matchings since every other monomial in $\poly_{G}^\kElem(\vct{X})$ has strictly less than $2\kElem$ distinct variables, which, as stated earlier implies that every other non-$\kElem$-matching monomial in $\rpoly_{G}^\kElem(\vct{X})$ has degree $< 2\kElem$.
Second, the only surviving monomials $\prod_{\ell=1}^\kElem X_{i_\ell}X_{j_\ell}$ of degree exactly $2k$ in $\rpoly_{G}^{\kElem}(\vct{X})$ must have that all of $i_1,j_1,\dots,i_\kElem,j_\kElem$ are distinct in $\poly_{G}^{\kElem}(\vct{X})$.
By the last two statements, only monomials composed of $2k$ distinct variables in $\poly_{G}^{\kElem}(\vct{X})$ (and hence of degree $2\kElem$ in $\rpoly_{G}^{\kElem}(\vct{X})$) correspond to a $k$-matching in $G$.
%It has already been established above that a $\kElem$-matching ($\kmatch$) has coefficient $c_{2\kElem}$. As noted, a $\kElem$-matching occurs when there are $\kElem$ edges, $e_1, e_2,\ldots, e_\kElem$, such that all of them are disjoint, i.e., $e_1 \neq e_2 \neq \cdots \neq e_\kElem$. In all $\kElem$ factors of $\poly_{G}^\kElem(\vct{X})$ there are $k$ choices from the first factor to select an edge for a given $\kElem$ matching, $\kElem - 1$ choices in the second factor, and so on throughout all the factors, yielding $\kElem!$ duplicate terms for each $\kElem$ matching in the expansion of $\poly_{G}^\kElem(\vct{X})$.
each of the $k!$ permutations of an arbitrary monomial maps to the same distinct $\kElem$-matching in $G$, and this implies a $\kElem!$ to $1$ mapping between degree $2\kElem$ monomials in $\rpoly_{G}^{\kElem}(\vct{X})$ and $\kElem$-matchings in $G$.
%we then have $\kElem!$ monomials mapped to each distinct $\kElem$-matching of $G$.
%Since $c_{2k}$ is the cardinality of the multi-set of degree $2\kElem$ monomials in $\rpoly_{G}^{\kElem}(\vct{X})$, where each $\kElem$-matching in $G$ has $\kElem!$ monomial representations,
It then follows that $c_{2\kElem}=\kElem!\cdot\numocc{G}{\kmatch}$.
%One can compute $\numocc{G}{\kmatch}$ in $\query_{G}^\kElem(\vct{X})$ exactly.
%\end{Corollary}
%
%\begin{proof}[Proof for Corollary ~\ref{cor:lem-qEk}]
%By ~\cref{lem:qEk-multi-p}, the term $c_{2\kElem}$ can be exactly computed. Additionally we know that $c_{2\kElem}$ can be broken into two factors, and by dividing $c_{2\kElem}$ by the factor $\kElem!$, it follows that the resulting value is indeed $\numocc{G}{\kmatch}$.
%\end{proof}
%
%\qed
%\begin{Corollary}\label{cor:tilde-q-hard}
%Computing $\rpoly(\vct{X})$ is $\#W[1]$-hard.
%\end{Corollary}
%
%\begin{proof}[Proof of Corollary ~\ref{cor:tilde-q-hard}]
%The proof follows by ~\cref{thm:k-match-hard}, ~\cref{lem:qEk-multi-p} and ~\cref{cor:lem-qEk}.
