  Object Storage Systems (OSS) inside a cloud promise scalability, durability,
availability, and concurrency. However, open-source OSS does not have a
specific approach to letting users and administrators search based on the data,
which is contained inside the object storage, without involving the entire
cloud infrastructure. Therefore, in this paper, we propose Sherlock, a novel
Content-Based Searching (CoBS) architecture to extract additional information
from images and documents. Here, we store the additional information in an
Elasticsearch-enabled database, which helps us to search for our desired data
based on its contents. This approach works in two sequential stages. First, the
data will be uploaded to a classifier that will determine the data type and
send it to the specific model for the data. Here, the images that are being
uploaded are sent to our trained model for object detection, and the documents
are sent for keyword extraction. Next, the extracted information is sent to
Elasticsearch, which enables searching based on the contents. Because the
precision of the models is so fundamental to the search's correctness, we train
our models with comprehensive datasets (Microsoft COCO Dataset for multimedia
data and SemEval2017 Dataset for document data). Furthermore, we put our
designed architecture to the test with a real-world implementation of an
open-source OSS called OpenStack Swift. We upload images into the dataset of
our implementation in various segments to find out the efficacy of our proposed
model in real-life Swift object storage.
