Indeksis

Indeksis is a term used in information retrieval to denote a family of index structures designed to scale fast search across large and heterogeneous datasets. The concept is not a single deployed technology but a set of design principles and architectures proposed in academic and experimental settings. The name suggests a plural or collective form of index (indeks) with a suffix that signals a system of multiple indices and their interrelations.

Core ideas behind indeksis include combining traditional inverted indexing with semantic representations and cross-language linking, enabling efficient keyword queries, phrase queries, and semantic similarity across languages. Indeksis systems typically use modular components: an indexing layer that builds multi-field inverted indices and vector representations; a query processor that fuses lexical and semantic signals; a ranking module that blends term-frequency-based scores with semantic similarity; and a storage layer capable of incremental updates and fault-tolerant operation. Some proposals emphasize multilingual alignment, using cross-lingual embeddings to map terms across languages, while others focus on domain-specific vocabularies and metadata-rich indexing.

Indexing strategies in indeksis often stress incremental updates, compressed storage, and partitioned architectures to support parallel query execution. These designs aim to improve scalability, adaptability to evolving corpora, and resilience in large-scale search environments. Applications discussed in research include digital libraries, enterprise search, and large-scale web or repository search, particularly where multilingual content and rich metadata are common. Indeksis remains largely in the realm of theoretical and prototype systems, with ongoing exploration of performance trade-offs and implementation challenges. See also inverted index, information retrieval, semantic search, multilingual indexing.