Bulk-bitwise processing-in-memory (PIM), an emerging computational paradigm utilizing memory arrays as computational units, has been shown to benefit database applications. This paper demonstrates how GROUP-BY and JOIN, database operations not supported by previous works, can be performed efficiently in bulk-bitwise PIM used for relational database analytical processing. We develop a gem5 simulator and show that our hardware modifications, on the Star Schema Benchmark and compared to previous works, improve, on average, execution time by $1.83\times$, energy by $4.31\times$, and the system's lifetime by $3.21\times$. We also achieved a speedup of $4.65\times$ over MonetDB, a modern state-of-the-art in-memory database.