Hadoop MapReduce framework is an important distributed processing model for large-scale data intensive applications. The current Hadoop and the existing Hadoop distributed file system’s rack-aware data placement strategy in MapReduce in the homogeneous Hadoop cluster assume that each node in a cluster has the same computing capacity and a same workload is assigned to each node. Default Hadoop doesn’t consider load state of each node in distribution input data blocks, which may cause inappropriate overhead and reduce Hadoop performance, but in practice, such data placement policy can noticeably reduce MapReduce performance and may increase extra energy dissipation in heterogeneous environments. This paper proposes a resource aware adaptive dynamic data placement algorithm (ADDP).With ADDP algorithm, we can resolve the unbalanced node workload problem based on node load status. The proposed method can dynamically adapt and balance data stored on each node based on node load status in a heterogeneous Hadoop cluster. Experimental results show that data transfer overhead decreases in comparison with DDP and traditional Hadoop algorithms. Moreover, the proposed method can decrease the execution time and improve the system’s throughput by increasing resource utilization.

Finding an optimized place is undeniably a momentous subject in establishing the marketing strategies of a retail store. Based on the existing literature, the process of selecting an optimized location for a business can be defined as a ranking problem that compares and rates existing or potential sites based on their ability to attract customers. Consequently, this article is concentrated on the evaluation of machine learning ranking methods in ranking existing retail stores based on the data derived from LBSNs. Using feature engineering techniques, we defined and calculated a set of features for 239 retail store branches in Tehran, from the venue data obtained from the Foursquare API. Additionally, we derived a rank for each store representing store popularity via user-generated data from Foursquare, Dunro, and Google Maps. Next, we implemented a number of classification and “ learn-to-rank” algorithms to rate these stores. Finally, by evaluating the prediction precision and ranking precision of the algorithms used, we analyzed the fit and prediction power of all ranking algorithms. The outcomes of this research suggest that most algorithms used are, in fact, reliable methods for ranking retail store sites. Therefore, such algorithms can be used as a technique for retail store site selection, given a list of existing or potential sites for a store. Additionally, our results clearly suggest a superiority in the ranking precision of “ learn-to-rank” algorithms for retail store placement. Out of all algorithms used, with a ranking precision of 0. 854, MART is the most powerful algorithm for ranking retail store sites.

Background. Immediate molar implants (IMIs) have been shown to provide an effective treatment, but their placement comes with potential anatomically related risks. Methods. CBCTs of>400 dental sites were analyzed for key anatomical features at mandibular molar sites that can impact the placement of IMIs. Features measured included distances from each molar furcation to points risking lingual plate perforation or inferior alveolar nerve (IAC) damage, distances from molar root apices to IAC, mesiodistal and buccolingual widths of molar inter-septal bone (ISB), and thicknesses of buccal and lingual cortical plates at first and second mandibular molar sites. Results. Distances from molar furcations to contact with lingual cortical plates and to IAC decreased significantly from mesial to distal, as did distances from root apices to the mandibular canal. Both buccolingual and mesiodistal ISB widths and thicknesses of buccal and lingual cortical plates increased mesiodistally. Buccolingual ISB widths were largest coronally for both molar sites and decreased apically. The reverse was found with mesiodistal septal ISB widths, which increased coronoapically. Conclusion. Risks of lingual perforations or IAC damage were significantly greater at second molars vs. first molars. The ability to place IMIs in ISB at first molars was estimated to be>twice as often as at second molars. Maximal implant lengths for IMIs placed in the furcal bone should not exceed 10 mm.

Background. This study used CBCT images to evaluate the suitability of maxillary first and second molar sites to receive immediate implants. Buccopalatal and mesiodistal widths of maxillary molar inter-radicular septum were evaluated at three different levels (crestal, middle, and apical), in addition to assessments of the root apex and furcation proximities to the sinus floor and comparisons of these measurements between the first and second upper molar sites before extraction. Methods. A total of 427 dental sites from 223 patients were used to measure the buccopalatal and mesiodistal widths of inter-septal/furcal (IRS) bone of maxillary first and second molars and vertical distances from the furcation and from all the root apices to the sinus floor (SF). Results. Mean coronal-most buccopalatal/mesiodistal IRS widths were 6. 52/7. 33 mm for the first and 5. 85/6. 86 mm for the second molars (P=0. 008). Corresponding mean FSD (furcation-sinus floor) values were 9. 69 mm (range: 24. 68-2. 02 mm) and 8. 84 mm (range: 25. 09-1. 48 mm). Mean distances from all the root apices to SF were <3 mm. The palatal roots of the first molars had higher sinus intrusion rates (%28. 85) than their buccal counterparts, while for the second molars, the mesiobuccal roots showed the highest sinus intrusion (%37. 65). Conclusion. In the current patient sample, %61. 7 of the first and %34 of the second molars had a sufficiently broad IRS to encase a-5mm-diameter IMI (immediate molar implant) completely. The mean FSD of 9 mm for both molars indicated that some sinus floor elevation would likely be needed unless short implants were used.