Index structures in memory systems become important to improve the entire system
performance. The promising learned indexes leverage deep-learning models to …

With high memory density, non-volatility, and DRAM-scale latency, persistent memory (PM)
is promising to improve the storage system performance. Hashing-based index structures …

Hybrid memory systems composed of DRAM and Non-Volatile Memory (NVM) promise the
capacity benefits of NVM and the low-latency benefits of DRAM. Most existing hash-based …

Non-volatile memory (NVM) is expected to coexist with DRAM as a hybrid memory to fully
exploit DRAM's low read–write latency and NVM's high density, persistence, and low …

Cuckoo filter (CF) and its variants are emerging as replacements of Bloom filters in various
networking and distributed systems to support efficient set representation and membership …

Persistent memory (PM) provides large-scale non-volatile memory (NVM) with DRAM-
comparable performance. The non-volatility and other unique characteristics of PM …

Efficient key-value (KV) store becomes important for concurrent and distributed systems to
deliver high performance. The promising learned indexes leverage deep-learning models to …

Ubiquitous computing systems possess the capability to collect and process data, which is
subsequently shared with other devices. These systems encounter resource challenges …

Cold data contributes a large portion of the big data today and is usually stored in secondary
storage. Various sketch data structures are implemented to represent the stored elements …

Cuckoo filter is a new type of approximate membership query data structure that stores the
fingerprints of set elements. Because of its higher performance than bloom filter, cuckoo filter …