The paper you provided has two important omissions: 1) it does not mention Tseitin transformation to encode Disjunctive Normal Form into a Conjunctive Normal Form and 2) does not look at decision diagrams, especially, zero-suppressed decision diagrams for set representation.
Tseitin transformation prevents exponential expansion in conversion from DNF to CNF.
The fact that paper's algorithm employs disjunctive normal form suggests the use of binary decision diagrams. ROBDDs represent DNFs naturally, for one example. ZDDs represent sets of sets and were relatively successfully used in non-trivial approaches to the SAT solving problem like [1].
Also, the paper you mentioned has this right in abstract: "However, there is still the quest of making the new worst-case optimal join algorithms truly practical in terms of (1) ease of implementation and (2) secondary index efficiency in terms of number of indexes created to answer a query."
WCO is hard to implement and it might be computation-wise prohibitive.
There are at least two different types of them, conflict-derived clause learning and variants and stochastic search and variants.
Which one is more similar to WCO join algorithm?