Scalable and Dynamic Regeneration of Big Data Volumes

A core requirement of database engine testing is the ability to create synthetic versions of the customer’s data warehouse at the vendor site. A rich body of work exists on synthetic database regeneration, but suffers critical limitations with regard to: (a) maintaining statistical fidelity to the client’s query processing, and/or (b) scaling to large data volumes. In this paper, we present HYDRA, a workload-dependent database regenerator that leverages a declarative approach to data regeneration to assure volumetric similarity, a crucial aspect of statistical fidelity, and materially improves on the prior art by adding scale, dynamism and functionality. Specifically, Hydra uses an optimized linear programming (LP) formulation based on a novel regionpartitioning approach. This spatial strategy drastically reduces the LP complexity, enabling it to handle query workloads on which contemporary techniques fail. Second, Hydra incorporates deterministic post-LP processing algorithms that provide high efficiency and improved accuracy. Third, Hydra introduces the concept of dynamic regeneration by constructing a minuscule database summary that can on-the-fly regenerate databases of arbitrary size during query execution, while obeying volumetric specifications derived from the query workload. A detailed experimental evaluation on standard OLAP benchmarks demonstrates that Hydra can efficiently and dynamically regenerate large warehouses that accurately mimic the desired statistical characteristics

G × E interaction studies under natural farming and inorganic production system in maize (Zea mays L.)

A study was conducted to analyze the interaction of twelve maize genotypes with the environment for fourteen traits. The crop was grown during the kharif season of 2021 and 2022 in six different environments using two production systems, natural farming and the inorganic production system, in Palampur and Kangra using randomized block design with three replications. G × E interaction and stability analysis following AMMI model exhibited significant variation due to genotypes and environments for all the traits, indicating the presence of sufficient variability among the genotypes and environments. In contrast, G × E interaction was significant for all the traits except for number of kernel rows per cob and ear circumference (cm). Results showed that significant variation was present among the genotypes and environments. The environment had greater impact on most traits, except number of kernel rows per cob, ear circumference (cm), 100- grain weight (g), harvest index (%) and protein (%).The mean squares for the IPCA 1, IPCA 2 and IPCA 3 cumulatively contributed more than 80% of the total G × E interaction for all the traits. Overall,Girija was found to be the most stable genotype for the maximum number of traits, followed by L-315 and L-316.Girija was most stable for grain yield per plant followed by L-315, L-316 and L-318 under the inorganic production system. Bajaura Makka followed by L-315 and L-316 were stable genotypes under the natural farming production system with high grain yield per plant. Both L-315 and L-316 were found to be suitable for cultivation under both farming methods

HYDRA: A Dynamic Big Data Regenerator

A core requirement of database engine testing is the ability to create synthetic versions of the customer’s data warehouse at the vendor site. Prior work on synthetic data regeneration suffers from critical limitations with regard to (a) scaling to large data volumes, (b) handling complex query workloads, and (c) producing data on demand. In this demo, we present HYDRA, a workload-dependent dynamic data regenerator, that materially addresses these limitations. It introduces the concept of dynamic regeneration by constructing a minuscule memory-resident database summary that can on-the-fly regenerate databases of arbitrary size during query execution. Further, since the data is generated in memory, the velocity of generation can be closely regulated. Finally, to complement dynamic regeneration, Hydra also ensures that the process of summary construction is data-scale-free

Isolated Amoebic Abscess of Spleen

Amoebic liver abscess is the most common extraintestinal manifestation of amoebiasis. Extrahepatic amoebic abscesses have occasionally been described in the lung, brain, and skin and presumably result from hematogenous spread. Isolated amoebic abscess of spleen has been reported scarcely in literature. We report here a case of isolated amoebic abscess of spleen

Scalable and Dynamic Regeneration of Big Data Volumes

A core requirement of database engine testing is the ability to create synthetic versions of the customer’s data warehouse at the vendor site. A rich body of work exists on synthetic database regeneration, but suffers critical limitations with regard to: (a) maintaining statistical fidelity to the client’s query processing, and/or (b) scaling to large data volumes. In this paper, we present HYDRA, a workload-dependent database regenerator that leverages a declarative approach to data regeneration to assure volumetric similarity, a crucial aspect of statistical fidelity, and materially improves on the prior art by adding scale, dynamism and functionality. Specifically, Hydra uses an optimized linear programming (LP) formulation based on a novel regionpartitioning approach. This spatial strategy drastically reduces the LP complexity, enabling it to handle query workloads on which contemporary techniques fail. Second, Hydra incorporates deterministic post-LP processing algorithms that provide high efficiency and improved accuracy. Third, Hydra introduces the concept of dynamic regeneration by constructing a minuscule database summary that can on-the-fly regenerate databases of arbitrary size during query execution, while obeying volumetric specifications derived from the query workload. A detailed experimental evaluation on standard OLAP benchmarks demonstrates that Hydra can efficiently and dynamically regenerate large warehouses that accurately mimic the desired statistical characteristics.This article is published as Sanghi, Anupam, Raghav Sood, Jayant Haritsa, and Srikanta Tirthapura. "Scalable and Dynamic Regeneration of Big Data Volumes," in Proceedings of the 21st International Conference on Extending Database Technology (EDBT), Vienna, Austria, March 26-29, 2018. DOI: 10.5441/002/edbt.2018.27. Posted with permission.</p

HYDRA: A Dynamic Big Data Regenerator

A core requirement of database engine testing is the ability to create synthetic versions of the customer’s data warehouse at the vendor site. Prior work on synthetic data regeneration suffers from critical limitations with regard to (a) scaling to large data volumes, (b) handling complex query workloads, and (c) producing data on demand. In this demo, we present HYDRA, a workload-dependent dynamic data regenerator, that materially addresses these limitations. It introduces the concept of dynamic regeneration by constructing a minuscule memory-resident database summary that can on-the-fly regenerate databases of arbitrary size during query execution. Further, since the data is generated in memory, the velocity of generation can be closely regulated. Finally, to complement dynamic regeneration, Hydra also ensures that the process of summary construction is data-scale-free.This proceeding is published as Sanghi, Anupam, Raghav Sood, Dharmendra Singh, Jayant R. Haritsa, and Srikanta Tirthapura. "HYDRA: A Dynamic Big Data Regenerator." Proceedings of the VLDB Endowment 11, no. 12 (2018): 1974. doi: 10.14778/3229863.3236238. Posted with permission.</p

Isolated Amoebic Abscess of Spleen

Amoebic liver abscess is the most common extraintestinal manifestation of amoebiasis. Extrahepatic amoebic abscesses have occasionally been described in the lung, brain, and skin and presumably result from hematogenous spread. Isolated amoebic abscess of spleen has been reported scarcely in literature. We report here a case of isolated amoebic abscess of spleen