{'fraction_non_alphanumeric': 0.01678183613030602, 'fraction_numerical': 0.0, 'mean_word_length': 5.07185628742515, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
JCAM: The Joined Clustered Access Method

Spatial data management has been an active area of intensive research for more than two decades. In order to support objects in a database system, several issues should be taken into consideration including indexing and efficient query processing. Several indexing techniques have been proposed in the literature. Most of the existing indexing mechanisms are designed for spatial selection but may not be efficient for join operations. In this paper, a new structure is proposed to handle join operations between spatial data sets, called the joined clustered access method (JCAM). JCAM is pre-joined index dedicated for answering spatial join queries designed to enhance the response time of spatial join queries by decreasing the number of disk accesses. JCAM is a secondary index used to represent relationships between data sets as a colored-graph. Experiments show that JCAM outperforms the RTJ algorithm which is based on R-tree but requires more construction time.