{
    "ok": true,
    "doi": "10.46243/jst.2022.v7.i01.pp111-120",
    "doi_display": "10.46243/jst.2022.v7.i01.pp111-120",
    "doi_url": "https://doi.org/10.46243/jst.2022.v7.i01.pp111-120",
    "state": "registered",
    "url": "https://www.jst.org.in/index.php/pub/article/view/246",
    "title": "Emerging Databases for Next Generation Big Data Applications",
    "version": 2,
    "registered_via": "crossref",
    "prefix": {
        "prefix": "10.46243",
        "status": "live"
    },
    "registrant": {
        "name": "Longman Publishers",
        "kind": "publisher",
        "country": "India"
    },
    "reserved_at": null,
    "registered_at": "2026-09-29 22:00:23",
    "updated_at": "2026-09-29 23:59:41",
    "withdrawn_at": null,
    "withdrawn_reason": null,
    "record": {
        "format": "smartscholars-doi-metadata/1.0",
        "doi": "10.46243/jst.2022.v7.i01.pp111-120",
        "referent": "Creation",
        "type": "JournalArticle",
        "structural_type": "Digital",
        "modes": [
            "Visual"
        ],
        "characters": [
            "Language"
        ],
        "titles": [
            {
                "value": "Emerging Databases for Next Generation Big Data Applications",
                "type": "PrincipalTitle",
                "lang": "en"
            }
        ],
        "identifiers": [
            {
                "type": "DOI",
                "value": "10.46243/jst.2022.v7.i01.pp111-120"
            }
        ],
        "agents": [
            {
                "role": "author",
                "name": {
                    "given": "Dr.Syed Abdul Sattar",
                    "family": "Dr.Syed Abdul Sattar"
                },
                "sequence": "first"
            },
            {
                "role": "publisher",
                "name": {
                    "org": "Longman Publishers"
                }
            }
        ],
        "dates": {
            "published": "2023-07-26",
            "date_type": "PublicationDate",
            "online": "2023-07-26"
        },
        "language": "en",
        "container": {
            "type": "Journal",
            "titles": [
                {
                    "value": "Journal of Science & Technology",
                    "type": "PrincipalTitle"
                }
            ],
            "identifiers": [
                {
                    "type": "ISSN",
                    "value": "2456-5660",
                    "medium": "electronic"
                }
            ],
            "volume": "7",
            "issue": "1",
            "pages": {
                "first": "111",
                "last": "120"
            }
        },
        "links": [
            {
                "url": "https://www.jst.org.in/index.php/pub/article/view/246",
                "return_type": "text/html",
                "primary": true
            },
            {
                "url": "https://www.jst.org.in/index.php/pub/article/download/246/215",
                "purpose": "text-mining",
                "return_type": "application/pdf"
            },
            {
                "url": "https://www.jst.org.in/index.php/pub/article/download/246/2339",
                "purpose": "text-mining",
                "return_type": "application/xml"
            },
            {
                "url": "https://jst.org.in/admin/uploads/4_CSE_2022_JAN_1_CS_OK-2.pdf",
                "purpose": "similarity-checking"
            }
        ],
        "abstract": {
            "value": "The rising quality of large -scale period analytics applications (real -time inventory/pricing, mobile apps that offer you suggestions, fraud detection, risk analysis, etc.) emphases the requirement for distributed knowledge management systems which will handle quick transactions and analytics simultaneously. Efficient process of transactional and analytical requests, however, need completely different optimizations and branch of knowledge selections in a system. This paper presents the wildfire system that targets Hybrid Transactional and Analytical process (HTAP). wildfire leverages the Spark system to modify large -scale processing with differing types of complicated analytical requests, and columnar processing to modify quick transactions and analytics simultaneously",
            "lang": "en"
        },
        "license": {
            "url": "https://creativecommons.org/licenses/by/4.0/",
            "start": "2023-07-26",
            "applies_to": "vor"
        },
        "references": [
            {
                "key": "ref1",
                "unstructured": "Aerospike. http://www.aerospike.com/"
            },
            {
                "key": "ref2",
                "unstructured": "Alluxio. http://www.alluxio.org/"
            },
            {
                "key": "ref3",
                "unstructured": "Amazon S3. https://aws.amazon.com/s3/"
            },
            {
                "key": "ref4",
                "unstructured": "Apache Cassandra. http://cassandra.apache.org"
            },
            {
                "key": "ref5",
                "unstructured": "Apache Hadoop. http://hadoop.apache.org/"
            },
            {
                "key": "ref6",
                "unstructured": "Apache Hadoop HDFS. http://hortonworks.com/apache/hdfs/"
            },
            {
                "key": "ref7",
                "unstructured": "Apache HBase. https://hbase.apache.org/"
            },
            {
                "key": "ref8",
                "unstructured": "Apache Kudu. https://kudu.apache.org/"
            },
            {
                "key": "ref9",
                "unstructured": "Apache Parquet. https://parquet.apache.org/"
            },
            {
                "key": "ref10",
                "unstructured": "Apache Phoenix. http://phoenix.apache.org/"
            },
            {
                "key": "ref11",
                "unstructured": "Apache Spark. http://spark.apache.org/"
            },
            {
                "key": "ref12",
                "unstructured": "DataStax Spark Cassandra Connector. https:"
            },
            {
                "key": "ref13",
                "unstructured": "//github.com/datastax/spark-cassandra-connector"
            },
            {
                "key": "ref14",
                "unstructured": "Hive Transactions. https://cwiki.apache.org/ confluence/display/Hive/Hive+Transactions. MemSQL. http://www.memsql.com/"
            },
            {
                "key": "ref15",
                "unstructured": "MyRocks. https://code.facebook.com/posts/190251048047090/myrocks-a-space-and-write-optimized-mysql-database/"
            },
            {
                "key": "ref16",
                "unstructured": "OpenStack Swift. https://www.swiftstack.com/product/openstack-swift"
            },
            {
                "key": "ref17",
                "unstructured": "Splice Machine. http://www.splicemachine.com/"
            },
            {
                "key": "ref18",
                "doi": "10.14778/2824032.2824137",
                "unstructured": "D. Abadi, S. Babu, F. O¨ zcan, and I. Pandis. Tutorial: SQL-on-Hadoop Systems. PVLDB, 8:2050–2051, 2015"
            },
            {
                "key": "ref19",
                "unstructured": "M. Armbrust, R. S. Xin, C. Lian, Y. Huai, D. Liu, J. K. Bradley, X. Meng, T. Kaftan, M. J. Franklin, A. Ghodsi, and M. Zaharia. Spark SQL: Relational"
            },
            {
                "key": "ref20",
                "unstructured": "Data Processing in Spark. In SIGMOD, pages 1383–1394, 2015"
            },
            {
                "key": "ref21",
                "unstructured": "J. Baker, C. Bond, J. C. Corbett, J. Furman, A. Khorlin, J. Larson, J.-M. Leon, Y. Li, A. Lloyd, and V. Yushprakh. Megastore: Providing Scalable, Highly"
            },
            {
                "key": "ref22",
                "unstructured": "Available Storage for Interactive Services. In CIDR, 2011"
            },
            {
                "key": "ref23",
                "unstructured": "R. Barber, M. Huras, G. M. Lohman, C. Mohan, R. Mueller, F. O¨ zcan, H. Pirahesh, V. Raman, R. Sidle, O. Sidorkin, A. Storm, Y. Tian, and P. To¨zu¨n"
            },
            {
                "key": "ref24",
                "doi": "10.1145/2882903.2899406",
                "unstructured": "Wildfire: Concurrent Blazing Data Ingest and Analytics. In SIGMOD, pages 2077–2080, 2016"
            },
            {
                "key": "ref25",
                "unstructured": "Published by: Longman Publishers www.jst.org.in"
            },
            {
                "key": "ref26",
                "unstructured": "P. Boncz, M. Zukowski, and N. Nes. MonetDB/X100: Hyper-Pipelining Query Execution. In CIDR, 2005"
            },
            {
                "key": "ref27",
                "doi": "10.1145/343477.343502",
                "unstructured": "E. A. Brewer. Towards Robust Distributed Systems. In PODC, pages 7–, 2000"
            },
            {
                "key": "ref28",
                "unstructured": "F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra, A. Fikes, and R. E. Gruber. Bigtable: A Distributed Storage"
            },
            {
                "key": "ref29",
                "doi": "10.1016/s0167-2789(06)00199-0",
                "unstructured": "System for Structured Data. In OSDI, pages 205–218, 2006"
            },
            {
                "key": "ref30",
                "unstructured": "L. Chang, Z. Wang, T. Ma, L. Jian, L. Ma, A. Goldshuv, L. Lonergan, J. Cohen, C. Welton, Sherry, and M. Bhandarkar. HAWQ: A Massively Parallel"
            },
            {
                "key": "ref31",
                "unstructured": "Processing SQL Engine in Hadoop. In SIGMOD, pages 1223–1234, 2014"
            },
            {
                "key": "ref32",
                "unstructured": "F. F a¨rber, N. May, W. Lehner, P. Große, I. Mu¨ller, Rauhe, and J. Dees. The SAP HANA Database – An Architecture Overview. IEEE DEBull"
            },
            {
                "key": "ref33",
                "unstructured": "S. Gray, F. O¨ zcan, H. Pereyra, B. van der Linden, and A. Zubiri. IBM Big SQL 3.0: SQL-on-Hadoop without compromise"
            },
            {
                "key": "ref34",
                "unstructured": "https://public.dhe.ibm.com/common/ssi/ecm/sw/en/ sww14019usen/SWW14019USEN.PDF, 2015"
            },
            {
                "key": "ref35",
                "doi": "10.1109/icde.2011.5767867",
                "unstructured": "A. Kemper and T. Neumann. HyPer – A Hybrid OLTP&OLAP Main Memory Database System Based on Virtual Memory Snapshots. In ICDE, pages"
            },
            {
                "key": "ref36",
                "unstructured": "M. Kornacker, A. Behm, V. Bittorf, T. Bobrovytsky, C. Ching, A. Choi, J. Erickson, M. Grund, D. Hecht, Jacobs, I. Joshi, L. Kuff, D. Kumar, A. Leblang"
            },
            {
                "key": "ref37",
                "unstructured": "Li, I. Pandis, H. Robinson, D. Rorke, S. Rus, J. Russell, D. Tsirogiannis, S. Wanderman-Milne, and M. Yoder. Impala: A Modern, Open-Source SQL Engine"
            },
            {
                "key": "ref38",
                "unstructured": "for Hadoop. In CIDR, 2015"
            },
            {
                "key": "ref39",
                "doi": "10.14778/2367502.2367518",
                "unstructured": "A. Lamb, M. Fuller, R. Varadarajan, N. Tran, B. Vandiver, L. Doshi, and C. Bear. The Vertica Analytic Database: C-store 7 Years Later. PVLDB"
            },
            {
                "key": "ref40",
                "unstructured": "N. Mukherjee, S. Chavan, M. Colgan, D. Das, M. Gleeson, S. Hase, A. Holloway, H. Jin, J. Kamp, K. Kulkarni, T. Lahiri, J. Loaiza, N. Macnaughton, V"
            },
            {
                "key": "ref41",
                "doi": "10.14778/2824032.2824061",
                "unstructured": "Marwah, A. Mullick, A. Witkowski, J. Yan, and M. Zait. Distributed Architecture of Oracle Database In-memory. PVLDB, 8(12):1630–1641, 2015"
            },
            {
                "key": "ref42",
                "doi": "10.1007/s002360050048",
                "unstructured": "P. O’Neil, E. Cheng, D. Gawlick, and E. O’Neil. The Log-structured Merge-tree (LSM-tree). Acta Inf., 33(4):351–385, 1996"
            },
            {
                "key": "ref43",
                "unstructured": "V. Raman, G. Attaluri, R. Barber, N. Chainani, D. Kalmuk, V. KulandaiSamy, J. Leenstra, S. Lightstone, S. Liu, G. M. Lohman, T. Malkemus, R"
            },
            {
                "key": "ref44",
                "unstructured": "Mueller, I. Pandis, B. Schiefer, D. Sharpe, R. Sidle, A. Storm, and L. Zhang. DB2 with BLU Acceleration: So Much More than Just a Column Store. PVLDB"
            },
            {
                "key": "ref45",
                "doi": "10.1109/icde.2010.5447738",
                "unstructured": "A. Thusoo, J. S. Sarma, N. Jain, Z. Shao, P. Chakka, N. Zhang, S. Anthony, H. Liu, and R. Murthy. Hive - A Petabyte Scale Data Warehouse Using"
            },
            {
                "key": "ref46",
                "unstructured": "Hadoop. In ICDE, pages 996–1005, 2010"
            },
            {
                "key": "ref47",
                "unstructured": "Z. Zhang. Spark-on-HBase: Dataframe Based HBase Connector. http://hortonworks.com/blog/ spark-hbase-dataframe-based-hbase-connector. (Basic"
            },
            {
                "key": "ref48",
                "unstructured": "Book/Monograph Online Sources) J. K. Author. (year, month, day). Title (edition) [Type of medium]. Volume(issue). Available: http://www.(URL)"
            },
            {
                "key": "ref49",
                "unstructured": "J. Jones. (1991, May 10). Networks (2nd ed.) [Online]. Available: http://www.atm.com"
            },
            {
                "key": "ref50",
                "unstructured": "(Journal Online Sources style) K. Author. (year, month). Title. Journal [Type of medium]. Volume(issue), paging if given. Available:"
            }
        ],
        "record": {
            "registrant": "Longman Publishers",
            "registered": "2024-02-16",
            "updated": "2026-09-17",
            "issue_number": 1,
            "source": "crossref-api",
            "source_agency": "Crossref (member 25296)"
        }
    },
    "record_sha256": "bb54f181942508407f767f61eba2e69c5b5f7489a7bd076472218380be03df7c",
    "handle": {
        "synced_at": "2026-10-01 18:12:32",
        "url": "https://www.jst.org.in/index.php/pub/article/view/246"
    },
    "links": {
        "record_page": "https://registry.smartscholars.in/record.php?doi=10.46243%2Fjst.2022.v7.i01.pp111-120",
        "system_metadata": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2022.v7.i01.pp111-120&as=system",
        "history": "https://registry.smartscholars.in/api.php?action=history&doi=10.46243%2Fjst.2022.v7.i01.pp111-120",
        "kernel_xml": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2022.v7.i01.pp111-120&as=xml"
    }
}