Peer-to-Peer Search and Information Retrieval Systems
1. Peer-to-Peer Search
Answer: Introduction
Peer-to-peer (P2P) search is an information retrieval approach in which documents and data are distributed across multiple peer nodes, and search operations are performed without a centralized server. Each peer acts as both a client and a server.
Concept of Peer-to-Peer Search
In a P2P system, data is stored locally on individual peers. When a user submits a query, it is forwarded to other peers in the network. Each peer searches its local data and returns relevant results. This approach is commonly used in distributed systems, file-sharing networks, and large-scale decentralized environments.
Key Characteristics of P2P Search
- Decentralized architecture: There is no central index or server controlling the search process.
- Distributed data storage: Documents are stored across multiple peer nodes.
Identify the Need of Information Retrieval
Answer: Introduction
Information retrieval (IR) is required to search, organize, and retrieve relevant information from large collections of data efficiently. With the rapid growth of digital information, manual searching has become impractical, making IR systems essential.
Need of Information Retrieval
- Rapid growth of information: Huge volumes of data are generated daily in the form of documents, web pages, emails, and multimedia. IR systems are needed to manage and search large data collections efficiently.
- Efficient search mechanism: IR provides fast search techniques to retrieve relevant documents instead of scanning entire databases manually.
- Relevance-based retrieval: IR systems retrieve information based on user queries and relevance ranking, not just exact keyword matching.
- Time saving: By filtering irrelevant data, IR systems help users save time and effort in finding useful information.
- Support for user information needs: IR systems help users satisfy their information needs, such as research, decision-making, learning, and problem-solving.
- Handling unstructured data: Most information is in unstructured form (text documents, web pages). IR techniques are needed to process and retrieve such data effectively.
4. Explain the Process of Indexing
Answer: Introduction
Indexing is the process of organizing documents into a structured form so that information can be retrieved quickly and efficiently. In information retrieval systems, indexing converts raw documents into an index structure (usually an inverted index).
Process of Indexing
The indexing process involves the following steps:
- Document collection: All documents (text files, web pages, articles, etc.) are collected and stored for processing.
- Tokenization: Documents are broken into individual terms or tokens (words). Example: “Information Retrieval System” → Information, Retrieval, System.
- Stop-word removal: Common words such as “is”, “the”, “and”, “of” are removed because they do not add meaningful information.
- Index construction: An inverted index is created that maps each term to the list of documents in which it appears.
6. Open Search Engine Frameworks for IR
Answer: Introduction
Open search engine frameworks are open-source tools used to index, search, and retrieve information efficiently from large document collections. They provide core IR functionalities such as indexing, querying, and relevance ranking.
Apache Lucene
Explanation: Apache Lucene is a high-performance, open-source text search library written in Java. It provides the core indexing and searching functionality used by many search engines.
Key Features
- Full-text indexing and searching
- Uses inverted index structure
- Supports ranking based on relevance
- Highly scalable and fast
- Platform independent
Limitation
It is a library, not a complete search engine (no UI or server by default).
Diagram: Lucene Architecture
- Documents
- Analyzer
- Inverted Index
- Query Engine
- Search Results