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This paper proposes a systematic full text search on document using a combined keyword and structural similarity of documents under consideration. The approach operates in two steps. The first step uses a set of designated keywords to acquire potential desired documents by means of an open source tool. The second step
structural dimensions. A cell in the text cube aggregates a set of documents with matching dimension values on a subset of dimensions. Given a keyword query, our goal is to find the top-k most relevant cells in the text cube. We propose a relevance scoring model and efficient ranking algorithms. Experiments are conducted to
with matching attribute values in a subset of dimensions. We define a keyword-based query language and an IR-style relevance model for scoring/ranking cells in the text cube. Given a keyword query, our goal is to find the top-k most relevant cells. We propose four approaches: inverted-index one-scan, document sorted-scan
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.