PARALLELIZING TIME-SERIES SESSION DATA ANALYSIS WITH A TYPE-ERASURE BASED DSEL

The Science Information Network (SINET) is a Japanese academic backbone network.  SINET consists of more than 800 universities and research institutions.  In the operation of a huge academic backbone network, more flexible querying technology is required to cope with massive time series session data and analysis of sophisticated cyber-attacks. This paper proposes a parallelizing DSEL (Domain Specific Embedded Language) processing for huge time-series session data. In our DESL, the function object is implemented by type erasure for constructing internal DSL for processing time-series data. Type erasure enables our parser to store function pointer and function object into the same *void type with class templates. We apply to scatter/gather pattern for concurrent DSEL parsing. Each thread parses DSEL to extract the tuple timestamp, source IP, and destination IP in the gather phase. In the scattering phase, we use a concurrent hash map to handle multiple thread outputs with our DSEL. In the experiment, we have measured the elapsed time in parsing and inserting IPv4 address and timestamp data format ranging from 1,000 to 50,000 lines with 24-row items. We have also measured CPU idle time in processing 100,000,000 lines of session data with 5, 10 and 20 multiple threads. It has been turned out that the proposed method can work in feasible computing time in both cases

Histomorphologic Tumor Regression and Lymph Node Metastases Determine Prognosis Following Neoadjuvant Radiochemotherapy for Esophageal Cancer: Implications for Response Classification

OBJECTIVE: We sought to quantitatively and objectively evaluate histomorphologic tumor regression and establish a relevant prognostic regression classification system for esophageal cancer patients receiving neoadjuvant radiochemotherapy. PATIENTS AND METHODS: Eighty-five consecutive patients with localized esophageal cancers (cT2-4, Nx, M0) received standardized neoadjuvant radiochemotherapy (cisplatin, 5-fluorouracil, 36 Gy). Seventy-four (87%) patients were resected by transthoracic en bloc esophagectomy and 2-field lymphadenectomy. The entire tumor beds of the resected specimens were evaluated histomorphologically, and regression was categorized into grades I to IV based on the percentage of vital residual tumor cells (VRTCs). A major response was achieved when specimens contained either less than 10% VRTCs (grade III) or a pathologic complete remission (grade IV). RESULTS: Complete resections (R0) were performed in 66 of 74 (89%) patients with 3-year survival rates of 54% ± 7.05% for R0-resected cases and 0% for patients with incomplete resections ortumor progression during neoadjuvant therapy (P < 0.01). Minor histopathologic response was present in 44 (59.5%) and major histopathologic response in 30 (40.5%) tumors. Significantly different 3-year survival rates (38.8% ± 8.1% for minor versus 70.7 ± 10.1% for major response) were observed. Univariate survival analysis identified histomorphologic tumor regression (P < 0.004) and lymph node category (P < 0.01) as significant prognostic factors. Pathologic T category (P < 0.08), histologic type (P = 0.15), or grading (P = 0.33) had no significant impact on survival. Cox regression analysis identified dichotomized regression grades (minor and major histomorphologic regression, P < 0.028) and lymph node status (ypN0 and ypN1, P < 0.036) as significant independent prognostic parameters. A 2-parameter regression classification system that includes histomorphologic regression (major versus minor) and nodal status (ypN0 versus ypN1) was established (P < 0.001). CONCLUSIONS: Histomorphologic tumor regression and lymph node status (ypN) were significant prognostic parameters for patients with complete resections (R0) following neoadjuvant radiochemotherapy for esophageal cancer. A regression classification based on 2 parameters could lead to improved objective evaluation of the effectiveness of treatment protocols, accuracy of staging and restaging modalities, and molecular response prediction