Hashing algorithms have been widely adopted for fast address look-up, which involves a search through a database to find a record associated with a given key. Hashing algorithms transforms a key into a hash value hoping that the hashing renders the database a uniform distribution with respect to the hash value. The closer to uniform hash values, the less search time required for a query. When t...

Due to the storage and retrieval efficiency, hashing has been widely deployed to approximate nearest neighbor search for large-scale multimedia retrieval. Supervised hashing, which improves the quality of hash coding by exploiting the semantic similarity on data pairs, has received increasing attention recently. For most existing supervised hashing methods for image retrieval, an image is first...

Hashing is one of the fastest table lookup techniques, not just in SAS®, but in any programming language. Figure 1 illustrates the concept of a table lookup and the speed advantage of SAS V9 Hashing over a format table lookup. If a programmer needs to select, from a large file, all subjects that are in a small file, hashing will likely save disk space and time. Hashing should be part of the too...

Nearest neighbor search aims at obtaining the samples in database with smallest distances from them to queries, which is a basic task range of fields, including computer vision and data mining. Hashing one most widely used methods for its computational storage efficiency. With development deep learning, hashing show more advantages than traditional methods. In this survey, we detailedly investi...

In this paper we evaluate performance of data-dependent hashing methods on binary data. The goal is to find a hashing method that can effectively produce lower dimensional binary representation of 512-bit FREAK descriptors. A representative sample of recent unsupervised, semi-supervised and supervised hashing methods was experimentally evaluated on large datasets of labelled binary FREAK featur...

A method for identifying graphs using MD5 hashing is presented. This allows fast graph equality comparisons and can also be used to facilitate graph isomorphism testing. The graphs can be labeled or unlabeled. The method identifies vertices by hashing the graph configuration in their neighborhoods. With each vertex hashed, the entire graph can be identified by hashing the vertex hashes.

This lecture note presents and analyses two simple hashing algorithms: “Hashing with Chaining”, and “Cuckoo Hashing”. The analysis uses only very basic (and intuitively understandable) concepts of probability theory, and is meant to be accessible even for undergraduates taking their first algorithms course.

We define a new simple and general model for hashing. The basic model, together with several variants capture many natural (sequential and parallel) hashing algorithms and represent common hashing practice. Our main results exhibit tight tradeoffs between hash table size and the number of applications of a hash function on a single key.

Each of the key values x comes from a universe U , i.e. x ∈ U . In this document, we assume U = {1, 2, . . . N}. Observe that the set S is a dynamic set. Each of the Insert and Delete operations may modify the set. Hence the size of the set S changes with each operation. We bound the maximum size of the set to n (n << N). What are the data structures that can be used to store the set S? One opt...

Emerging non-volatile memory technologies (NVMs) have been considered as promising candidates for replacing DRAM and SRAM, due to their advantages of high density, high scalability, and requiring near-zero standby power, while suffering from the limited endurance and asymmetric properties of reads and writes. The significant changes of low-level memory devices cause nontrivial challenges to hig...